The Role: As a Software Engineer on our AI Infrastructure team, you will help design the core systems that power Prism AI’s generative AI platform. You will help build infrastructure and tools that ensure the reliability, performance, quality, and availability of our AI system. Our mission is to make Prism AI the most reliable and user friendly generative AI platform in the world. You will partner closely with our cloud infrastructure team, product team, and performance team to deliver infrastructure that bridges the gap between our customer and the ultra-performant proprietary Prism inference engine. Key Responsibilities: Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines Build and maintain core backend services such as LLM CI/CD pipeline, control plane, and model serving systems Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers Building frameworks and safeguards to ensure Prism AI has the best model quality in the industry Collaborate with performance, training, and product teams to translate research and product needs into infrastructure solutions Participate in code reviews, technical discussions, and continuous integration and deployment processes Minimum Qualifications: Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience) 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems Strong programming skills in Python, Go, or a similar language Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes, etc.). Basic understanding of LLM knowledge (e.g., context length, disaggregated prefill, KV cache memory estimation, etc) Preferred Qualifications: 5+ years of experience in software engineering, with a focus on infrastructure or machine learning systems Experience with open source inference engine like vLLM, Sglang, or TRT-LLM Contributions to open-source infrastructure or ML projects Experience in building large scale ML/MLOps infrastructure

NO AGENCIES - Freelancers only. Looking for an Azure engineer who can help manage a client's existing Azure Infrastructure and help out with Azure DevOps CI/CD pipelines. Hours are flexible - about 20-25 hours a week. Only hard requrirement is that you are able to attend their daily standup at 10am CST

We are hiring an AI Engineer with strong hands-on experience building and shipping real AI products. Requirement: If you don't have a GitHub profile to share, this role is not a fit. What we’re looking for: • Strong experience in AI/ML engineering • Ability to build, test, and deploy production-ready AI systems • Practical experience working on real-world AI projects To apply: Please share your portfolio, past AI projects, and relevant work samples. Applicants without portfolio will be ignored.

We are hiring an AI Engineer for a remote opportunity with our Airlines project. The ideal candidate should have hands-on experience building GenAI solutions, including RAG pipelines, vector embeddings, prompt engineering, MCP server development, and integrating multiple LLM providers. Experience working with AWS Neptune (Graph DB), OpenSearch (Vector Store), Redis, REST APIs, and SSE-based streaming services is required. Exposure to LangChain, MCPSharp, or ModelContextProtocol.SDK is a plus. If interested, please share your updated resume along with your total years of experience, years of GenAI experience, RAG experience, MCP/Agentic AI experience, current location, work authorization, and availability to start.

I want to build a private multi-model RAG-based Opportunity Intelligence Agent. It should support document ingestion, opportunity-specific workspaces, vector search, source citations, multi-model routing across OpenAI, Claude, Perplexity, and possibly DeepSeek, and generate strategic recommendations from both uploaded files and live web research. This is intended to become a reusable base agent capable of knowledge retrieval, web research, multi-model orchestration, document analysis, citation generation, and agent clonding and configuration. It will be used for analyzing & strategy development for project opportunities, responding to RFPs, and proposal assistance, as well as other applications.

About the Role We're looking for an experienced AI/ML developer to design, build, and deploy production-grade AI systems. You'll work on LLM-powered applications, retrieval-augmented generation (RAG) pipelines, and multi-agent architectures. This is a hands-on role for someone who has shipped real AI products—not just prototypes. Responsibilities Design and implement RAG pipelines (chunking, embeddings, vector stores, retrieval optimization) Build multi-agent systems using LangChain and LangGraph Integrate and fine-tune LLMs (OpenAI, Anthropic, open-source models) Develop and optimize prompts, tool-calling, and agent orchestration logic Deploy scalable, reliable AI services with proper monitoring and evaluation Collaborate on architecture decisions and code reviews Required Skills (Mandatory) Strong AI/ML fundamentals and hands-on experience Proven LLM application development RAG pipeline design and optimization LangChain and LangGraph (production experience) Agent / multi-agent system build experience Vector databases (Pinecone, Weaviate, Chroma, FAISS, or similar) Python (production-level) API integration (OpenAI, Anthropic, Hugging Face) Nice to Have Fine-tuning / LoRA / model optimization Cloud deployment (AWS/GCP/Azure) MLOps and evaluation frameworks (LangSmith, RAGAS) Experience with streaming, caching, and cost optimization

Sr. Azure Cloud Architect / Engineer — Part-Time Freelance Location: Remote Engagement: Part-time / freelance / project-based Availability: Ongoing, as-needed support Company: SKYTEK Solutions, LLC About the Role We are looking for a senior-level Azure Cloud Architect / Engineer to assist our team on a part-time freelance basis with advanced Azure infrastructure, security, automation, and cloud architecture projects. This is not an entry-level or basic admin role. We need someone who is highly experienced, hands-on, and comfortable working in real enterprise environments. The ideal candidate has deep experience designing, securing, deploying, and supporting Azure IaaS environments using Microsoft best practices, the Cloud Adoption Framework, Terraform/IaC, runbooks, CI/CD pipelines, and strong security controls. You will work alongside our internal engineering team on client cloud environments, architecture reviews, migrations, automation, hardening, and ongoing optimization. Responsibilities The selected consultant will assist with: Designing and reviewing Azure IaaS environments Azure landing zone architecture using Microsoft Cloud Adoption Framework principles Hub-and-spoke network design, VPN, routing, NSGs, Azure Firewall, and private networking Terraform / Infrastructure as Code development and review Azure DevOps or GitHub-based CI/CD pipelines for infrastructure deployment Azure Automation runbooks and operational automation Security hardening of Azure environments Identity and access design using Entra ID, PIM, RBAC, MFA, and Conditional Access Backup, disaster recovery, monitoring, alerting, and operational readiness Cost optimization, rightsizing, and Azure best-practice reviews Documentation of architecture, runbooks, deployment processes, and support procedures Assisting with complex troubleshooting and escalation support Required Experience The right candidate must have strong hands-on experience with: Microsoft Azure IaaS Azure Cloud Adoption Framework Azure landing zones Terraform / IaC Azure DevOps and/or GitHub Actions CI/CD pipelines Azure Automation runbooks Azure networking Entra ID / Azure AD RBAC, PIM, Conditional Access, MFA Azure security best practices Backup, DR, monitoring, logging, and governance Enterprise client environments Preferred Experience Strong preference for candidates with experience in: MSP or consulting environments Multi-tenant or multi-client Azure support Azure Migrate Windows Server workloads in Azure Active Directory integration with Azure Defender for Cloud Sentinel / SIEM integrations Policy-as-Code Bicep, ARM templates, or PowerShell automation SOC 2 / ISO 27001 aligned environments Regulated industries such as healthcare, finance, or enterprise manufacturing Ideal Candidate We are looking for someone who is: Senior, experienced, and confident Hands-on, not just theoretical Security-focused Excellent at documenting work Able to explain complex cloud decisions clearly Comfortable working with an MSP engineering team Reliable, responsive, and professional Able to jump into existing environments and quickly assess what is needed Focused on doing things the right way, not quick-and-dirty cloud builds Engagement Details This is a part-time freelance role with ongoing project work. Hours may vary based on client needs, but we are looking for someone who can become a trusted senior cloud resource for advanced Azure architecture and engineering support. Availability during some US business hours is preferred. How to Apply Please include: A brief summary of your Azure architecture experience Examples of Azure IaaS or landing zone projects you have completed Your experience with Terraform and CI/CD pipelines Your experience with Azure security, CAF, and governance Your hourly rate and general availability Any relevant Microsoft certifications Screening Questions Please answer the following: Describe an Azure landing zone you designed or implemented. What architecture did you use? How do you structure Terraform for enterprise Azure deployments? What are the most important Azure security controls you implement by default? Have you built CI/CD pipelines for Azure infrastructure deployment? Please explain. How do you approach cost optimization and governance in Azure?

**Overview** We are a fast-growing SaaS company with a lean engineering team (~10 devs) utilizing a modern Python (FastAPI/Django) and Node.js backend, React frontend, and PostgreSQL stack. We have already deployed an initial multi-model agent stack—Claude Code, LiteLLM gateway, Git worktrees, and MCP integrations. We need an expert to run an intensive architecture review and optimization session for our current infrastructure. We are not looking for someone to build a full-time, weeks-long project from scratch. Instead, we need a seasoned engineer who has shipped this exact type of infrastructure end-to-end to audit our setup, identify architectural gaps, and guide our team on hardened implementation. This project must move fast. If your timeline is measured in weeks, please do not apply. We want someone who looks at this scope, jumps into a review session, and delivers actionable architectural guidance in days. This starts as a focused, urgent consultation. However, we expect ongoing advisory work—follow-ups, architecture adjustments, and enhancement reviews—as the AI tooling landscape shifts. For the right engineer, this will turn into a recurring relationship. We are completely open to a fixed price per milestone or an hourly structure. **What You Need to Have Actually Shipped and Can Review (Not Just Read About)** * **Full Agentic Coding Harnesses:** The entire loop: orchestrator → subagent → CI gate → merge loops. * **Isolation Layers:** Configured execution layers (such as E2B, Modal, or secure Docker runtimes) as isolated sandboxes for AI-generated code. * **Parallel Claude Code Sessions:** Managed multiple simultaneous subagents on scoped tasks via Git worktrees. * **Self-Hosted LiteLLM Gateways:** Routing to multiple models (Claude, GPT, Gemini, DeepSeek). * **MCP Server Infrastructure:** Connected file system, PostgreSQL, Atlassian, and Slack tool layers for active agents. * **Agent Framework Structures:** Used CLAUDE.md, COMMON\_MISTAKES.md, subagent role definitions, hook scripts, and settings.json. * **Human-in-the-Loop Orchestration:** Built Plan Mode or equivalent approval gates before agent execution. * **Multi-Agent Frameworks:** 7-agent feature factory patterns or frameworks like LangGraph, CrewAI, or Autogen. * **Durable Workflow Engines:** Applied Temporal, n8n, or similar tools for long-running agent workflow execution. * **Mechanical Quality Gates:** Treating CI green as the ultimate gate for agent output quality. \[[1](https://manveerc.substack.com/p/ai-agent-sandboxing-guide)\] **Our Current Stack (What you are reviewing)** * **Backend:** Python (FastAPI / Django) & Node.js (TypeScript) * **Frontend:** React (Next.js) * **Database & ORM:** PostgreSQL / Prisma / SQLAlchemy * **Infrastructure:** Docker Compose, AWS (ECS/EKS) * **CI/CD:** GitHub Actions / GitLab CI * **AI Layer:** Claude Code with shared `.claude/` directory, CLAUDE.md, and LiteLLM gateway in Docker * **MCP:** Atlassian (Jira/Confluence), custom PostgreSQL MCP server, Slack * **Workflow Automation:** Temporal / n8n * **QA Automation:** Playwright / Autonoma **Scope of Work (Review & Advisory Only)** 1. **Comprehensive Audit:** Audit our current agent harness and identify architectural gaps against a production-grade standard. 2. **Sandbox Strategy Consultation:** Review our environment strategy to ensure highly secure, isolated execution runtimes for agent code runs. 3. **Workflow Hardening Review:** Evaluate our parallel agent workflow setup (Git worktrees, subagent role configs, hook scripts, and settings lockdown). 4. **CI Pipeline Integration Strategy:** Advise on wiring our sandbox execution layer into the existing CI pipeline so agent-executed code runs in clean snapshots, not live infra. 5. **Architectural Runbook:** Deliver an optimization report / documented standard that our backend lead can easily own and execute going forward. **How to Apply** Skip the generic pitch. Show us something real to be considered: 1. A GitHub repo, architecture diagram, or Loom walkthrough of an agentic harness you have actually shipped. 2. Specific tools from our stack you have personally configured (E2B, LiteLLM, Claude Code, etc.). 3. One sentence explaining the hardest problem you solved to get full agent loops running reliably. 4. Your availability to conduct this high-impact architectural review session this week.

Senior AI/ML Engineer / Claude architect — Legal Tech FirmProfit AI is the operational backbone of the modern law firm. We automate law firm operations end to end, and we're looking for a top-tier AI/ML engineer to help us build the next major platform in legal tech. We need a true expert. Someone deeply proficient with Claude and modern LLM architecture who has shipped real products at a high level. You're fluent across the full stack with Node.js, React, Postgres, MongoDB etc... and you have hands-on experience building with LangChain, LangGraph, MCP, and AWS Bedrock. We're not looking for someone who's read about LLMs. We're looking for someone who has shipped agents, orchestration layers, and production AI systems that real users depend on every day. Our current team is 8 engineers, we have firms signed and live, and we're moving fast. This is a chance to come in early, and have your work in the hands of customers within weeks. Contract to start, with a long-term path for the right person. Reply with the most impressive AI product you've shipped.

About the Role Assembly Software is a B2B SaaS company serving law firm customers and is actively expanding its internal AI capabilities. We are seeking a highly skilled AI contractor to serve as our embedded AI program lead — someone who can own and advance the design, implementation, and governance of AI tooling across the entire organization. This is a hands-on, strategic role. You will work directly with IT leadership and cross-functional teams to assess our current AI landscape, close gaps, and build a mature, secure, and operationally excellent AI program. We are a heavy Anthropic/Claude shop. Strong familiarity with Claude, the Anthropic API, and the Model Context Protocol (MCP) ecosystem is a significant advantage for this role. Core Responsibilities • Audit existing AI tool usage and identify overlaps, gaps, and shadow IT • Design and implement a company-wide AI governance framework • Lead MCP server setup, integration, and lifecycle management • Configure and manage Claude Teams/Enterprise deployments • Build and maintain an internal AI Skill Library for staff use • Define AI security policies and data access controls • Evaluate and recommend new AI tools and vendors • Establish prompt engineering standards and best practices • Connect AI tooling to internal business systems (Salesforce, M365, Asana, and others) • Support AI integrations with sensitive data sources including our data warehouse and CRM • Produce documentation, SOPs, and executive-ready reporting • Train internal staff and stakeholders on AI capabilities and safe usage Required Qualifications • Hands-on AI implementation experience in enterprise environments • Deep familiarity with large language model platforms, particularly Anthropic Claude and OpenAI • Proven experience building and managing MCP (Model Context Protocol) servers and integrations • Strong understanding of AI security — data exposure risks, access scoping, governance controls, and audit logging • Experience integrating AI tooling with business systems such as Salesforce, Microsoft 365, or similar platforms • Ability to author clear governance documentation, security policies, and executive-facing deliverables • Comfortable operating independently with minimal oversight while maintaining strong stakeholder communication Preferred Qualifications • Hands-on experience with the Anthropic Claude API, including system prompt design, tool use, and agentic workflows • Background in B2B SaaS, legal technology, or other regulated industries • Familiarity with SOC 2 compliance requirements as they relate to AI tooling and data access • Prior experience standing up internal AI assistants or Copilot-style tooling connected to live business data • Knowledge of data warehousing and secure query patterns for LLM-to-database integrations • Familiarity with CI/CD workflows and lightweight DevOps for deploying AI services