Our agency is seeking an AI/ML engineer to support a growing portfolio of projects. You will help design, develop, and deploy machine learning solutions, collaborate with cross-functional teams, and contribute to scalable AI systems. The ideal candidate has experience building practical models, improving performance, and translating business needs into technical solutions. This is a long-term opportunity for someone who can work independently, communicate clearly, and help drive impactful AI initiatives.

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.

We are looking for a Senior AI Engineer to help us build and improve AI-powered applications. What you will do: Build AI applications using Python and LLMs Work with OpenAI, Claude, DeepSeek, and other AI models Build RAG systems and AI agents Connect AI models with APIs and databases Test and compare different AI models Improve AI performance, accuracy, and reliability Deploy AI applications to the cloud Fix bugs and improve existing AI systems Requirements: Strong experience with Python Experience with LLMs and Generative AI Experience with RAG and AI agents Experience with APIs and databases Experience with AWS, Azure, or GCP Good understanding of AI model testing and evaluation Ability to work independently and solve technical problems Nice to have: Experience with DeepSeek or open-source AI models Experience with LangChain or LlamaIndex Experience with vector databases Experience with Docker and Kubernetes Experience with AI model fine-tuning We are looking for someone with real project experience, not just theoretical AI knowledge. Please send us a short description of your experience and examples of AI projects you have worked on.

The objective is to develop an AI model that detects and classifies Longitudinal and Transverse Cracking (LTC) in asphalt airfield pavements, in accordance with ASTM D5340-24. The model will use fused Digital Elevation Model (DEM) and imagery data to identify cracks, measure their extent, and assign a severity level (Low, Medium, or High) based on the standard's width and spalling criteria. The output will support the broader Pavement Condition Index (PCI) workflow used across the company's 85-model system. What you'll need to do: • Access and understand the provided DEM and imagery data, along with existing labels • Build a pipeline that fuses DEM and imagery to detect cracks and measure their length and width • Train a model to classify each detected crack as Low, Medium, or High severity per the ASTM thresholds • Apply the standard's exclusion rule so distresses are not double-counted where other cracking is already recorded • Validate the model's outputs against the team's verified ground truth and report accuracy • Deliver the trained model along with a short write-up of results and any limitations

PhD AI/ML Engineer / Computer Scientist — Scientific ML & Physical Systems We are an early-stage technology company seeking an exceptional AI/ML engineer, computer scientist, or computational scientist to help develop the intelligence layer of a hardware-based technology platform currently in POC development. This is not an LLM, chatbot, or generative-AI project. The work involves machine learning applied to real-world physical systems and sensor data. The Role You will work directly with our hardware/firmware engineer to: Develop AI/ML models for complex physical-system data Apply physics-informed and scientific machine-learning techniques Analyze multi-channel time-series data Develop signal-processing and feature-extraction pipelines Build and evaluate anomaly and state-estimation models Develop multimodal modeling approaches Optimize models for edge/embedded deployment Integrate the intelligence layer with existing hardware and firmware Help develop the POC into a robust production architecture Ideal Background PhD strongly preferred in Machine Learning, Computer Science, Computational Physics, Electrical Engineering, Applied Mathematics, Signal Processing, or a related discipline. We are particularly interested in experience with: Physics-Informed / Scientific ML Signal Processing Time-Series ML Sensor Fusion Anomaly Detection Physical-System Modeling Embedded ML / Edge AI PyTorch / TensorFlow / JAX NumPy / SciPy C/C++ / Embedded Systems Candidates who have successfully taken ML from research into real-world physical hardware are strongly preferred. Current Stage The hardware POC and data-acquisition system are already under development. The selected candidate will work directly with our hardware/firmware engineer to develop, validate, and integrate the AI/ML layer. There is potential for a significant ongoing role for the right person. To Apply Please briefly answer: What is the most relevant scientific-ML or physical-system ML project you have personally built? What portions did you personally design and implement? What experience do you have with real-world sensor/time-series data? Have you deployed ML onto embedded or edge hardware? Please provide relevant publications, GitHub repositories, patents, or other technical work. Application details, hardware architecture, use case, datasets, system objectives, and other proprietary information will be disclosed only to selected candidates following an NDA.

Global Positive News (GPN) is seeking an experienced AI Automation Engineer / AI Agent Developer to build an automated AI-powered newsroom for our rapidly growing positive-news media company.

I built a working website prototype with AI tools. What I am looking for is someone who works the way I want to work, several levels up: an AI engineer who can take something built this way and make it real, secure, and maintainable. To be clear about what I am not looking for. I do not want to hand a spec to a web development shop and receive a finished website. I want a partner who is fluent in building with AI, who can tell me where the AI-generated foundation under this thing is fine and where it is quietly dangerous, and who can get the intelligence at the center of the product working properly. I want to be in the room while it is built rather than handed the keys at the end. Where the project is today A self-contained single-file web application, roughly 100KB, built with AI tools, that runs in any browser with no server Around fifteen pages behind a client-side router A working assistant that answers plain-language questions and returns structured results, currently running on hand-written rules rather than a model All content lives in JavaScript arrays inside the file No hosting, no database, no backend, no authentication The assistant is the product The assistant needs to run on a live model, answer only from my data, and never invent a fact. Users will ask vague, badly-phrased questions using the wrong vocabulary, and it has to handle that without guessing. One failure mode I have already hit in testing: a query returned three correct results and silently left out four others that also qualified. Nobody watching knew what was missing. Getting that class of problem solved, and provably solved, is the core of this job. So I need someone with real opinions about retrieval and grounding, system prompt design, refusal behavior, handling ambiguous input, and how you test any of it. Specifically: how do I know the assistant is right, and how do I know a change I make next month did not break it? What I need built Everything built in my accounts and under my ownership from day one: repository, hosting, domain, database, model API keys A real stack and hosting on a custom domain, with a deployment process I can run A database as the single source of truth, with the content migrated out of the current file The assistant on a live model, server-side, with proper key handling, retrieval grounded in my data, refusal behavior when the answer is not there, rate limiting and cost controls An evaluation approach for the assistant. A test set, a way to catch regressions, and a way to measure completeness rather than just plausibility A way for non-technical contributors to add and correct content without touching code Security done properly and documented in plain language, including the AI-specific parts: API keys never reaching the client, prompt injection, what the model can and cannot see, abuse and runaway cost, plus the ordinary work of TLS, headers, secrets, backups, restores and dependency hygiene You are a good fit if you have Shipped LLM-powered products to production, not just demos Built retrieval over your own structured data, and have been burned by it Strong views on evaluating model output, and a practical method rather than a vibe Worked with non-technical founders and can explain a tradeoff without dumbing it down or burying me Fluency with the current AI build tools, and honesty about their limits In your proposal, please answer these four questions. Link one LLM-powered product you took to production. What was the architecture, and what broke in ways you didn't expect? My assistant returned three correct results and silently omitted four that also qualified. Diagnose that from what I have told you, and tell me how you would prevent the whole category, not just the instance. How do you evaluate an assistant like this? I want the actual method, including how you catch a regression after a change. What is your honest read on the fastest path: rebuild on a proper stack, or extend what exists? Tell me why. Assume I will ask follow-up questions. How this starts A paid two- to three-hour working session where you walk me through your read of the prototype and the architecture you would propose. I will share the prototype under NDA once we are talking. If this goes well there is likely ongoing work beyond the initial build.

The use case is AI-assisted prior authorization document preparation for specialty medication or imaging order. We have healthcare data in FHIR / EHR export format, including patient demographics, diagnosis codes, medications history, lab results, provider notes, previous treatments, allergies, and encounter history. The system should review this data and help generate a prior authorization support summary for clinical staff, showing why the request meets medical necessity. The workflow should be able to extract relevant clinical evidence from the chart, match it against authorization requirements, and generate structured output such as: - Patient and diagnosis summary - Requested medication / procedure - Relevant ICD/CPT/NDC codes if available - Previous failed therapies or contraindications - Supporting labs, notes, and visit history - Missing documentation or data needed before submission - Draft medical necessity summary for staff review We need someone to check our current AWS architecture and implement this workflow inside existing AWS setup. The implementation may use AWS Bedrock, SageMaker, Comprehend Medical, Lambda, S3, API Gateway, or other AWS services depending on what fits best. This work requires real healthcare software experience, not only general AI experience. HIPAA compliant design is important, including PHI handling, encryption, IAM permissions, secure data movement, CloudWatch logging, audit trail, and no unsafe storage of sensitive patient data.

We are seeking an experienced Lead Software Engineer to transform a proof-of-concept AI-driven commercial real estate platform into a scalable, production-ready product. The role involves architecture design, full-stack development, and integrating AI workflows to support decision-making. General Information We are building an AI-assisted decision intelligence platform for commercial real estate. The platform combines organizational, financial, real estate data, and AI recommendations to help nonprofits optimize their real estate decisions. The initial proof of concept has been completed on AWS, and the company now aims to develop a reliable V1. Tasks and Deliverables - Review existing codebase, AWS architecture, and product roadmap. - Identify components to retain, refactor, or rebuild for production. - Define the technical architecture for V1. - Develop front-end and back-end features, including workflows with product design input. - Expand assessment, scoring, dashboard, benchmarking, and scenario-planning capabilities. - Build AI-assisted workflows using large language models (LLMs) and business logic. - Implement document ingestion and data extraction capabilities. - Integrate proprietary and third-party datasets. - Develop authentication, permissions, organization accounts, and collaboration features. - Enhance testing, security, monitoring, reliability, and deployment processes. - Establish engineering standards and documentation. - Collaborate on team scaling decisions and onboarding additional engineers. Required Experience - Required: - Extensive experience building and shipping production SaaS products. - Full-stack development with Python, JavaScript/TypeScript, and modern frameworks such as React. - Proficiency with PostgreSQL and AWS services (including Lambda, containerized services). - Experience with multi-tenant SaaS architecture. - Knowledge of AI application development, including LLMs, Amazon Bedrock, and agent-based workflows. - Experience with data pipelines, document ingestion/extraction, authentication, and security. - Nice to have: - Background in fintech, proptech, enterprise SaaS, or decision-support platforms. - Experience turning AI proof of concepts into scalable production products. - Familiarity with LangChain, Retrieval-augmented Generation (RAG), and API integrations.

We are looking for a forward-thinking SEO Guru to optimize our SaaS platform for the modern search landscape. You must be an expert in traditional Google SEO and emerging Generative Engine Optimization (GEO) to help our product rank in Google SERPs and surface as a top recommendation in ChatGPT, Claude, Perplexity, and Gemini. 🎯 Key ResponsibilitiesTraditional SEO: Conduct keyword research, optimize technical SEO, and execute an aggressive content and backlink strategy.AI Engine Optimization (GEO): Optimize our digital footprint so AI models cite and recommend our SaaS.Brand Mention Strategy: Build high-authority citations, reviews, and mentions across platforms that LLMs use for training data.Competitor Analysis: Reverse-engineer how competitors win AI recommendations and Google snippets.Performance Tracking: Setup tracking for traditional keyword rankings and AI share-of-voice metrics. 💻 Required ExperienceSaaS Background: Proven track record of scaling organic traffic specifically for B2B or B2C SaaS.GEO Expertise: Practical experience optimizing content for AI search engine visibility and retrieval-augmented generation (RAG) systems.Technical SEO: Strong grasp of schema markup, site architecture, and entities.Content Strategy: Ability to build authoritative, deeply informational content hubs. ❓ Application Questions (Please answer when applying)Can you share a specific example of how you successfully optimized a website or product to appear in ChatGPT or Perplexity responses?What is your exact strategy for improving a SaaS company's "brand authority" for AI training models?What SEO tools do you rely on most for SaaS growth?