Principal AI Infrastructure & HPC Engineer We are a PE-backed, AI-native technology services company building a new AI Infrastructure & HPC practice across AWS and Microsoft Azure. We're looking for a deeply technical engineer to help build the practice from the ground up. This is not a traditional cloud or DevOps role. The focus is large-scale GPU infrastructure, distributed AI workloads, high-performance networking, and getting expensive compute environments to perform at their potential. What You'll Work On GPU cluster benchmarking, performance tuning and optimization NCCL benchmarking and tuning AWS EFA and Azure GPU/HPC infrastructure InfiniBand, RDMA, RoCE and GPUDirect RDMA CUDA, NVLink/NVSwitch and NVIDIA GPU environments Distributed AI training and inference optimization Linux, kernel, driver and systems-level performance Kubernetes/EKS/AKS and Slurm-based GPU environments GPU cloud / neocloud infrastructure What We're Looking For We want someone with deep hands-on expertise in GPU/HPC systems who can benchmark an environment, identify where performance is being lost, and fix it. Experience with NCCL, CUDA, InfiniBand/RDMA, distributed training, Linux performance engineering, and large multi-node GPU clusters is particularly relevant. Deep AWS and/or Microsoft Azure experience is a major plus, particularly experience designing or optimizing GPU/HPC workloads using EFA, EC2 accelerated computing, Azure GPU infrastructure, EKS/AKS, and high-performance networking. You don't need to check every box. Depth in this domain matters more than breadth. More Than a Project We're building a practice around this capability. The right person can play an important role in defining our technical offerings, developing repeatable optimization methodologies, working directly with AWS and Microsoft, and helping us build the engineering team as the practice grows. If you've worked deep in GPU infrastructure, HPC, distributed systems, or high-performance networking, we'd like to talk.

🚫 Please apply only if you are the engineer who will personally do the work. We will not consider agencies, consulting firms, account managers, or subcontracted teams. We're a small growth equity firm based in Connecticut. Our team is using more AI tools internally, and we now need someone senior to help us build the secure Azure infrastructure underneath them. This role is more infrastructure, network, and security focused than AI development. We need someone who is very strong with Azure networking, Entra ID, Key Vault, private endpoints, VNets, firewalls, RBAC, managed identities, and secure access between internal systems. You should also be comfortable with DevSecOps, GitHub-based deployments, secrets management, logging, monitoring, and keeping production environments locked down without making them painful to use. The AI side matters because this environment will run LLM applications, agents, MCP servers, model APIs, and possibly some private or local models. We don't need someone to build prompts or chatbots. We need someone who understands how to securely host and operate AI infrastructure in production. Our systems connect to Microsoft 365, SharePoint, Outlook, CRM data, internal databases, and other business tools, and we handle confidential deal information. We care a lot about network isolation, identity, least-privilege access, auditability, and making sure one compromised service or agent cannot access everything else. This would start around 10-20 hours per week and could grow into an ongoing role. We're looking for an individual engineer, not an agency, and need reasonable overlap with Eastern Time. We'll likely start with a small paid trial around one part of the environment.

Senior Azure Platform / DevOps Engineer About Makai Makai uses best-in-class AI and data to solve real-world operational and strategic challenges at scale. We help businesses with enterprise automation, human-machine teaming, product design and development, and market intelligence. Until now, people have had to learn and adapt to software. Makai creates solutions that adapt to people because technology will never be 100% accurate. People are essential for all of our human-machine teaming solutions. About the role We build and run cloud platforms for our clients. A lot of the time that means standing up a reusable Terraform foundation and adapting it for each client and environment. You'd own these platforms: design them, build them from an empty subscription, keep them running, and act as the person who actually knows how the whole thing fits together. This is a senior role with a lot of trust attached. The clients differ, the topologies differ, the pipelines differ. What stays constant is Azure and the expectation that you can size up an unfamiliar setup, make the right calls, and own the result. The ideal candidate will be able to Stand up new platforms from scratch: network, compute, data, and the pipeline to ship to them. Make the architecture calls and live with them. That includes knowing when a Terraform change is a safe in-place update and when it's a destroy-and-replace you should never run blind against production. Run migrations that carry real risk. Move a live platform onto a new foundation, read the plan correctly, write the runbook, and have a rollback ready before you start. Keep platforms healthy over time: provider upgrades, drift, security, and not letting a shared foundation rot as more clients land on it. Work directly with the client's own DevOps engineers. Often they're the ones running the apply and you're advising, so you need to explain a plan, unblock a failure quickly, and be someone the engineer holding the keys trusts. Qualifications Azure experience is the hard requirement. You've built and run production Azure yourself, not just used modules someone else wrote. Specifically: Azure networking is where most of the work lives: VNets and subnets, private endpoints and private DNS zones, NSGs and UDRs, NAT, Application Gateway and WAF, load balancers, and hub-and-spoke / Virtual WAN. Terraform on Azure, with state treated as something you respect: workspaces, migrations, imports, remote backends, and the discipline not to force an apply through a lifecycle guard. The delivery path: Azure Container Registry, VM Scale Sets and cloud-init, and CI/CD with Azure DevOps or GitHub. Azure data services: Postgres Flexible Server, managed Redis, Blob storage, and a real sense of their sizing, failover, and cutover risks. Entra ID and RBAC: app registrations, service principals, role assignments scoped properly across subscriptions. Clear writing and a level head on a live call. You can hand someone a runbook they can follow without you in the room. Nice-to-haves Azure certs (AZ-104, AZ-305). Temporal or similar workflow runtimes, and scripting (Bash, Python) for the glue. Past consulting or client-embedded work. You've been the outside expert before. How we work Small senior teams, real ownership. We'd rather have one engineer who understands a platform top to bottom than several who each own a slice. Core values for Makai employees Be a flexible, innovative and creative thinker Be congenial and a team player Be self-sufficient Be self-driven with an ownership mentality Have a strong work ethic

We are looking for a skilled, hands-on AI Engineer to help us build and optimize our AI product. You will be responsible for designing the AI architecture, integrating modern LLMs/frameworks, and ensuring our AI pipeline runs efficiently, reliably, and accurately in production. Responsibilities Design, build, and deploy custom AI solutions (LLM integration, RAG, AI agents, or fine-tuning). Build robust prompt engineering pipelines, function-calling workflows, or structured output mechanisms. Implement vector databases (e.g., Pinecone, Weaviate, Qdrant, ChromaDB) for semantic search and retrieval. Optimize latency, API costs, and context window efficiency across LLM providers (OpenAI, Anthropic, open-source models). Connect AI models to backend services via REST APIs / webhooks. Implement evaluation metrics (hallucination detection, retrieval accuracy, output validation). Required Skills & Qualifications Languages: Python (strong expertise required), TypeScript/Node.js (a plus). AI / ML Tooling: LangChain, LlamaIndex, AutoGen, CrewAI, or direct SDK integrations (OpenAI, Anthropic, Hugging Face). Databases: Vector databases (Pinecone, Chroma, Qdrant, pgvector) + relational/NoSQL DBs. Deployment & Cloud: Docker, AWS / GCP / Azure, FastAPI / Flask, Serverless architectures. Core Concepts: In-depth understanding of Embeddings, RAG, Fine-Tuning, Function Calling, and Agentic Workflows. Preferred (Nice to Have) Experience deploying open-source models locally or on dedicated hardware (vLLM, Ollama, Hugging Face TGI). Experience with fine-tuning techniques (LoRA, QLoRA). Background in frontend AI UI integration (Vercel AI SDK, Streamlit, Gradio).

We are looking for an experienced AI Architect / Senior LLM Engineer to design and build an enterprise-grade AI platform for the healthcare industry. You will lead the architecture and implementation of intelligent AI solutions that improve clinical operations, automate administrative workflows, and enable healthcare professionals to access trusted medical knowledge through advanced AI technologies. The ideal candidate has hands-on experience building production-ready Agentic AI systems, Multi-Agent architectures, RAG pipelines, and LLMOps using modern AI frameworks and cloud platforms. Responsibilities Design and develop scalable Agentic AI solutions for healthcare applications. Build Multi-Agent Systems using LangGraph, CrewAI, or AutoGen. Develop enterprise Retrieval-Augmented Generation (RAG) pipelines for medical knowledge retrieval. Create AI agents for clinical knowledge assistance, document intelligence, workflow automation, and care coordination. Build and integrate MCP servers and custom AI tools with internal healthcare systems. Optimize prompt engineering, retrieval strategies, and response quality for high accuracy. Implement AI guardrails, evaluation pipelines, monitoring, and observability for production deployments. Deploy secure, scalable AI infrastructure on AWS using Infrastructure as Code and CI/CD best practices. Collaborate with engineering, product, and healthcare stakeholders to deliver reliable AI solutions. Required Skills 5+ years of experience in AI/ML or Generative AI development. Strong expertise in Python and backend API development. Experience with LangGraph, CrewAI, AutoGen, or similar multi-agent frameworks. Hands-on experience with AWS Bedrock, Azure OpenAI, or Vertex AI. Strong understanding of RAG architectures, vector databases, embeddings, and semantic search. Experience with Pinecone, Weaviate, pgvector, or similar vector databases. Knowledge of LLMOps, evaluation frameworks, prompt engineering, and AI observability tools. Experience with Docker, Terraform, CI/CD, and cloud-native deployments. Familiarity with healthcare compliance, security, and responsible AI practices is highly preferred. Preferred Technologies LangGraph CrewAI AutoGen AWS Bedrock Claude GPT-4o Gemini Pinecone pgvector LangSmith Arize Phoenix FastAPI Docker Terraform GitHub Actions MLflow Nice to Have Experience developing AI-powered healthcare platforms. Knowledge of healthcare workflows, clinical documentation, or medical knowledge systems. Experience integrating AI solutions with enterprise applications through APIs and MCP. Familiarity with AI governance, model evaluation, and production monitoring. If you are passionate about building enterprise-scale AI solutions that transform healthcare through Agentic AI and Generative AI, we'd love to hear from you.

About Us Paragon International, Inc. is a U.S.-based manufacturer of commercial concession equipment and food service products. We receive purchase orders from customers such as Amazon, Home Depot, distributors, school systems, and other commercial customers. Orders arrive by email in many different formats, including PDFs, Word documents, Excel spreadsheets, scanned documents, and occasionally photographed purchase orders. We are looking for an experienced AI Automation Engineer to design and build a production-ready system that automates our entire order intake process. This is not a simple chatbot project. We need someone who has successfully built business automation systems that combine AI, OCR, document processing, APIs, and workflow automation. Project Overview The system will monitor one or more Gmail inboxes continuously and automatically process incoming emails and attachments. The workflow should: * Monitor Gmail 24/7 for new incoming emails. * Download all attachments automatically. * Read: * PDF files * Microsoft Word documents * Excel spreadsheets * Scanned PDFs * Image files (JPG, PNG, TIFF, etc.) * Photographs of purchase orders * Use OCR when required. * Use AI to determine whether the email is: * Purchase Order * Quote Request * Cancellation * Return/RMA * Customer Inquiry * Other * Identify the customer automatically. * Extract all order information into a standardized data structure. * Detect duplicate purchase orders. * Automatically print valid purchase orders to our network printer. * Save documents into organized folders. * Rename files using a consistent naming convention. * Move processed emails into Gmail folders/labels. * Generate logs for auditing and troubleshooting. ## Future Phases The initial project focuses on reliable document processing and printing. Additional phases may include: * Sage 100 ERP integration * Automatic sales order creation * Inventory verification * Customer acknowledgment emails * Shipping workflow automation * Dashboard and reporting * AI exception handling * Multi-location printing We are looking for a long-term development partner who can continue improving the system over time. ## Required Skills Please apply only if you have strong experience with most of the following: * OpenAI API / ChatGPT API * Gmail API * OCR technologies (Tesseract, Azure Document Intelligence, Google Vision, AWS Textract, or similar) * Intelligent Document Processing (IDP) * PDF parsing * Workflow automation * Python * REST APIs * Windows automation * Network printing * Error handling and logging * AI document classification Experience with the following is a significant advantage: * n8n * Microsoft Power Automate * Make.com * ERP integrations * Sage 100 * Purchase Order processing * Manufacturing or distribution businesses ## Deliverables The completed solution should: * Run continuously with minimal supervision. * Be reliable enough for production use. * Handle errors gracefully. * Be well documented. * Be easy for our staff to maintain. * Be scalable as our order volume grows. ## To Apply Please include: 1. A description of similar automation projects you have completed. 2. Which automation platform you recommend (Python, n8n, Power Automate, Make, or another solution) and why. 3. Examples of AI document processing or OCR projects you've built. 4. Your experience integrating with ERP systems. 5. Your estimated timeline. 6. Your hourly rate or fixed-price proposal. Please begin your proposal with the phrase: **"I have built AI document automation systems."** This helps us identify applicants who have carefully read the project description. We are looking for a long-term partner, not just someone to complete a single project. If this project is successful, additional work will include ERP integration, warehouse automation, customer service automation, purchasing automation, and AI-driven business process improvements.

We are looking for an experienced API Integration Engineer to help finalize and optimize integrations for our security platform. The ideal candidate will have strong experience working with third-party APIs, authentication mechanisms, cloud-based AI services, and troubleshooting production integrations. Your primary responsibility will be to validate and configure API credentials for URL classification and IP reputation services, identify and integrate the correct Large Language Model (LLM) endpoint (OpenAI, Claude, Azure OpenAI, or custom/internal models), and ensure the overall system is secure, reliable, and high performing. This is a short-term contract with the potential for ongoing work if the engagement is successful. Responsibilities 1. Verify and configure API credentials for: - URL Classification services - IP Reputation services - Threat Intelligence APIs 2. Validate authentication methods including: - API Keys - OAuth 2.0 - Bearer Tokens - JWT 3. Identify the correct LLM provider and endpoint, including: - OpenAI - Claude (Anthropic) - Azure OpenAI - Google Gemini - Internal/custom LLM deployments 4. Confirm that all required API keys, secrets, and access tokens are correctly configured. 5. Test API connectivity and verify successful authentication. 6. Troubleshoot integration issues across development and production environments. 7. Optimize API performance, latency, retry mechanisms, and error handling. 8. Collaborate closely with our development team to resolve integration challenges. 9. Document the configuration process and provide recommendations for future maintenance. 10. Ensure best practices for credential management and secure secret storage. Required Skills 1. Strong experience integrating REST APIs 2. Experience with authentication protocols: - API Keys - OAuth2 - JWT - Bearer Tokens 3. Experience working with AI APIs including one or more of: - OpenAI - Anthropic Claude - Azure OpenAI - Google Gemini 4. Familiarity with URL reputation and threat intelligence services 5. Experience integrating IP reputation APIs 6. Strong debugging and troubleshooting skills 7. Knowledge of HTTP/HTTPS, JSON, webhooks, and API testing tools (Postman, Insomnia, etc.) 8. Experience with Python, Node.js, or similar backend technologies 9. Familiarity with cloud environments (AWS, Azure, or GCP) To Apply Please include the following in your proposal: - Brief overview of your experience with API integrations. - Examples of projects involving OpenAI, Claude, Azure OpenAI, or other LLM integrations. - Experience integrating URL classification, IP reputation, or cybersecurity APIs. - Your preferred development stack. We are looking for a highly skilled engineer who can quickly identify integration issues, ensure secure API connectivity, and help us deliver a robust, production-ready solution. If you have strong experience with API authentication, AI integrations, and troubleshooting complex systems, we'd love to hear from you.

Role Level: IC3 Role Title: Security Administrator II (US Contract) Reports to: Manager of Engineering Services Salary: $41/h Job Description The Security Administrator role supports BEMO managed service customers and internal teams by assisting in the implementation, management, and monitoring of security and compliance solutions across Microsoft 365 and hybrid environments. This role is focused on a security-centric customer base, and we are specifically seeking candidates with experience working in GCC High tenants. In this role, you will also have the opportunity to lead compliance frameworks, including SOC 2, ISO, and CMMC, by maintaining security and compliance requirements across regulated environments. The Security Administrator II IC3 will demonstrate the ability to conduct routine work with specialist and commercial knowledge in the following areas: • Microsoft 365 Security Administration • Azure • GRC Platforms • AI tools • Customer Service • Managed Services • Team Communication • Data Gathering and Analysis At BEMO, the Security Administrator IC3 competencies require: • Understanding of prioritization and time management of tasks • Building effective working relationships within the team and with peers • Demonstrates skill to influence other peers • Conducts complex tasks autonomously • Works on problems of moderate scope and uses multiple known practices and procedures to solve problems with the support of manager and peers • The ability to respond to customers’ security and compliance needs proactively and reactively in the alignment of BEMO’s products and service scope • Clear and open communicator with wider teams and stakeholders • Maintains transactional communication with customers or partners • Builds self-awareness about strengths and areas of development by being open to feedback from your manager and peers. • Consistently seeks to improve technical knowledge in the Microsoft technology and security areas Responsibilities & Primary Goals • Monitoring and Maintenance o Proactively secure Microsoft 365 and Azure environments o Monitor all security systems and provide advice on strategy and implementation for the customer base o Conduct security risk and vulnerability assessments on security package customers o Enforce data governance o Patch and vulnerability-managed life cycle o Implement updates programmatically on different security packages offered by BEMO o Send out customer communications on security improvements and maintenance • Automation and Implementation of Managed Service Solutions o Create and document repeatable processes through automation across our managed service maintenance activities o Manage internal projects, provide technical guidance o Must be comfortable performing multiple initiatives simultaneously in a fast-paced environment o Leverage AI and automation technologies to optimize processes, improve response times, and enhance overall managed service delivery • Cross-Group Collaboration and Support o Support the Customer Success team with customer-specific data for security scores and value realization efforts o Provide T1-T2 Team members support for tickets and issues relevant to managed service customers' security and compliance o Working with the BEMO IT Manager to align security policies and processes o Work collaboratively with delivery engineers, operations team members, customer success managers, support engineers, and our BEMO customers o Manage support queue during designated times • Managed Security o Triage: Working with our SOC team and Microsoft Sentinel, you will help filter the noise to prioritize incidents and alerts that matter to alleviate alert fatigue. o Investigate: Investigate and analyze the most critical incidents, and document progress and findings. You will be analyzing logs within M365 tools and Sentinel. o Respond: Contain and mitigate incidents faster with managed response and proactive remediation. o Prevent: Provide detailed recommendations and best practices to go beyond detection and response to prevent future attacks Requirements • Educational degree or diploma in Computer Science, Engineering, or the equivalent in proven experience • 2 + years of experience administrating, managing, and implementing Microsoft Azure and Microsoft 365 as an implementation, security, or support engineer. • Experience analyzing M365 usage data to identify issues and usage patterns • Strong critical thinking, analysis, and problem-solving skills • Strong competency in core professional skills, especially attention to detail, responsiveness, follow-through, and flexibility, with a high degree of emotional intelligence and tact • Ability to work independently and collaboratively with other internal teams when needed • Proven customer service experience with clear and consistent writing, presentation, and communication skills • Azure Cloud experience Specialized Knowledge or Skills Preferred • A Bachelor of Science or Engineering in Computer Science or a related field preferred • Other Microsoft certifications are preferred (AZ-500, SC-300, SC-400, etc.)

Integration Engineer Position Summary We are seeking an Integration Engineer to design, develop, and maintain integrations between software systems and external applications. The ideal candidate has strong software development fundamentals, experience working with APIs and cloud-based applications, and the ability to independently troubleshoot and learn new technologies. Responsibilities Build and maintain software integrations between systems. Develop and consume REST and SOAP APIs. Implement integration workflows based on technical requirements. Develop cloud-based services and applications. Integrate with third-party APIs using various authentication methods. Transform, validate, and map data between systems. Work with JSON, XML, CSV, and other structured data formats. Implement secure file transfer processes when needed. Develop error handling, logging, monitoring, and retry processes. Work with relational databases and SQL. Write unit and integration tests. Participate in code reviews. Troubleshoot technical and integration issues through resolution. Use source control and maintain technical documentation. Work with technical leadership to clarify requirements and identify technical issues. Use AI-assisted development tools to improve development and troubleshooting efficiency. Required Qualifications Professional software development experience or equivalent technical experience. Experience building and consuming REST APIs. Proficiency in at least one backend programming language, such as C#, Python, JavaScript/TypeScript, or Java. Familiarity with a major cloud platform such as Azure, AWS, or Google Cloud. Understanding of HTTP, REST, JSON, and common integration patterns. Understanding of API authentication and authorization. Working knowledge of SQL and relational databases. Experience transforming and validating data between systems. Experience with Git or another source control system. Understanding of software testing, debugging, and error handling. Familiarity with secure software development practices. Ability to learn unfamiliar technologies and APIs independently. Ability to work from technical requirements and specifications. Strong problem-solving and troubleshooting skills. Strong written and verbal communication skills. Preferred Qualifications Experience with API gateway or API management technologies. Experience with OAuth 2.0, OpenID Connect, or mTLS. Experience with cloud-managed databases and cloud storage. Experience with secrets management and managed identities. Experience with application monitoring and centralized logging. Experience with SFTP or other secure file transfer technologies. Experience with asynchronous processing, queues, or event-driven architectures. Experience with CI/CD pipelines and automated deployments. Experience integrating SaaS applications. Experience with EDI/X12. Experience with regulated or security-sensitive environments. Desired Characteristics Self-directed and able to work independently. Quick to learn new technologies and systems. Strong technical curiosity and problem-solving ability. Comfortable working through ambiguity and asking questions when needed. Produces clean, maintainable, and well-documented code. Takes ownership of work from development through testing and troubleshooting. Understands the importance of security, reliability, and data integrity. Comfortable using AI-assisted development tools while independently validating results. Works well with others and is receptive to feedback.

We're hiring a senior AI developer to build and deploy AI solutions for a fintech/credit-union platform. The work spans autonomous banking agents, fraud detection, credit scoring, and bill-pay/invoice automation — at the intersection of LLMs, cloud infrastructure, and financial-domain expertise, with security and compliance built in from the start. This is a long-term, ongoing engagement. What you'll do: AI agents & orchestration - Design, build, and deploy multi-agent systems using Amazon Bedrock Agents, LangChain, and related frameworks - Architect agentic workflows for core banking use cases: credit scoring, fraud detection, bill-pay automation, invoice management - Define agent personas, memory strategies, tool-use patterns, and escalation paths for production banking agents LLM engineering - Fine-tune, prompt-engineer, and evaluate LLMs for financial-domain tasks - Build RAG pipelines over credit-union knowledge bases, policy docs, and member data - Implement guardrails, content filtering, and compliance checks for safe, regulated outputs - Monitor performance, hallucination rates, and latency against SLAs Cloud infrastructure (AWS & Azure) - Architect and manage AI/ML workloads on AWS (Bedrock, SageMaker, Lambda, S3, IAM, VPC) and Azure (OpenAI Service, Azure ML, AKS) - Design secure, cost-optimized environments compliant with NCUA, PCI-DSS, and SOC 2 - Implement infrastructure-as-code with Terraform or AWS CDK DevOps & MLOps - Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, CodePipeline, Azure DevOps) - Containerize services with Docker, orchestrate with Kubernetes (EKS/AKS) - Apply MLOps best practices: model versioning, A/B testing, canary deployments, automated rollback - Stand up observability with logging, tracing, and alerting Python development - Write clean, well-tested Python for AI pipelines, REST APIs, and data workflows - Build FastAPI/Flask microservices exposing agent capabilities to frontend and core banking systems - Integrate with financial data sources, core banking APIs, and third-party fintech services Banking applications - Build credit-scoring models using alternative data and explainable AI (XAI) - Develop real-time fraud detection with behavioral analytics, anomaly detection, and auto-decisioning - Create conversational agents for bill pay, account management, and member self-service - Automate invoice workflows: extraction, classification, approval routing, reconciliation - Partner with compliance/risk to keep AI decisions auditable, fair, and regulatory-compliant What you should have: - 5+ years software engineering; 3+ years in AI/ML or LLM engineering - 2+ years building AI for banking, credit unions, or financial services - Hands-on experience with Amazon Bedrock, LangChain, Python, AWS, and infrastructure-as-code - Working knowledge of NCUA, PCI-DSS, SOC 2, GLBA, and Fair Lending requirements - Bachelor's or Master's in Computer Science, Software Engineering, Data Science, or related field Nice to have: - AWS or Azure AI/ML certifications - Open-source LLM experience (Llama, Mistral, Phi) and self-hosted inference (vLLM, Ollama) - Vector databases (Pinecone, OpenSearch, pgvector) - Graph-based fraud networks and graph ML - AI governance / responsible AI framework experience - Prior work at a credit union, community bank, or fintech lending platform To apply, please share: - Your resume highlighting AI and banking project experience - A brief note on your most impactful AI agent or LLM project in a financial-services context - Links to GitHub, portfolio, or published papers (optional but encouraged)