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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Generative AI Engineer - **Company:** DivIHN Integration - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Databases, Data Infrastructure, Data Mining, Software Design Documents, DevOps, Design of User Interfaces, Junit, Python (Programming Language), PostgreSQL, Machine Learning, Performance Tuning, Redis, Next.js, Unstructured Data, WebSocket, Google Cloud, Pytorch, ReactJS, Flask (Web Framework), Large Language Models, Prompt Engineering, Software Application Programming, Generative AI, Backend, Git, Fastapi, Pytest, Containerization, AI Platforms, Kubernetes, Low Latency, Machine Learning Operations, Front End Software Development, Restful APIs, Streamlit Framework, Docker, Web Api, Microservices - **Published:** August 18, 2026 - **Apply:** https://www.dice.com/job-detail/610c68ca-e0f0-4019-a6ee-ddb4b5b0e761 ## About the Role * Relevant Experience & STEM Foundation: 4+ years of professional software/ML engineering experience, with a dedicated AI/ML focus in the last 1-2 years. * Google Gemini / Vertex AI (Non-negotiable): Hands-on experience with the Gemini model family and Vertex AI, including deployment, grounding, and integration into production AI services. Hands-on experience with containerization (Docker) and deploying services via Cloud Run or GKE (Kubernetes). * Languages & AI Libraries: Proficiency in Python and modern ML/AI frameworks (PyTorch, LangChain, LangSmith) for building autonomous LLM agents, tools, and RAG pipelines. * Agent Building & Tool Calling: Proven experience building AI/LLM agents and tool-calling systems in Python against unstructured, multi-source data. * Context Engineering & RAG: Expertise in RAG pipelines, prompt engineering, context tuning, grounding, and Vector Databases (e.g., Milvus, Postgres/Pgvector). Clear understanding of advanced RAG architecture including Hybrid Search (Vector + Keyword), Re-ranking models, and semantic caching. * Unstructured Data Handling (Non-negotiable): Demonstrated ability to ingest, clean, extract, and structure text, tables, and images from unstructured documents (PDFs, design docs, regulatory files) for LLM training and usage., 1. Full Stack Engineering - Building applications for AI-powered services (Backend APIs + Front-End Integration). 2. Generative AI & LLM Platforms - 2+ years building RAG pipelines & LLM apps on Google Gemini / Vertex AI using Python, LangChain, and LangSmith. 3. Agent Building & Unstructured Data - Building AI agents/tool-calling systems in Python and handling unstructured, multi-source data extraction (PDFs, docs). Preferred Skills : 1. Direct experience architecting and serving custom REST APIs. 2. Experience in regulated / compliance-sensitive domains (e.g., healthcare / medical device guidelines like FDA, ISO 13485). 3. Experience integrating multiple LLM APIs beyond a single provider (OpenAI, Bedrock, Claude, or similar). Additional Preferred Skills * Direct experience designing and deploying high-throughput REST APIs (e.g., FastAPI/Flask). * Familiarity with medical device development regulations and compliance (e.g., FDA guidelines, ISO 13485). * Experience integrating multiple LLM APIs beyond a single vendor (e.g., OpenAI, AWS Bedrock, Anthropic Claude). * Front-end development/integration experience for UI design (e.g., Streamlit, Gradio, React/Next.js integration). Education Requirements: * Minimum Bachelors in Software/Computer/IT/Systems/Biomedical Engineering + 3 years Required Testing: * Technical evaluation of Python proficiency, RAG architecture concepts, and API/Agent design. ## Description Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered. Day to Day Responsibilities, * RAG & Prompt Engineering: Craft and refine effective prompts for RAG, grounding, and context tuning to achieve optimal AI performance in product development. * High-Performance API Engineering: Develop asynchronous microservices (FastAPI) using Server-Sent Events (SSE) or WebSockets to stream real-time LLM responses to front-end UIs without backend timeouts. * Vector Database Infrastructure: Design, develop, and implement robust Vector Databases using LLMs and modern retrieval technologies to capture information from diverse engineering sources (PDFs, design docs, regulatory guidelines). * Data Extraction & Structuring Pipelines: Build and optimize pipelines to extract and structure multi-modal data (tables, text, images) from unstructured documents for LLM training, grounding, and runtime query execution. * LLM Fine-Tuning & Training: Fine-tune and train generative AI models using client's engineering data and domain knowledge to create high-accuracy, domain-specific models. * GenAI Application & Tool Development: Design and implement scalable backend APIs (FastAPI/REST) and UI integration interfaces so internal engineers can query knowledge bases and analyze data. * Automated Requirements Generation: Develop backend functionalities to automatically generate technical requirements from design documents, user stories, and system specification files. * Documentation & Knowledge Transfer: Thoroughly document architecture, code, REST endpoints, and model training procedures to enable seamless knowledge transfer to client's internal teams. * Cross-Functional Collaboration: Partner closely with Subject Matter Experts (SMEs), System Engineers, and V&V Test teams to optimize AI-powered workflows. * AI Guardrails, MLOps & Cost Governance: Implement hallucination checks, PII masking, and guardrails (e.g., NeMo Guardrails) for medical device context. Track token usage, latency, and costs using LangSmith or Vertex AI monitoring., * AI & Agent Frameworks: PyTorch, LangChain, LangSmith, Vertex AI SDK. * API & Web Frameworks: FastAPI, Flask, REST APIs, Server-Sent Events (SSE). * Databases & Search: Milvus, Pgvector, Qdrant, Redis (caching). * Front-End Integration: Streamlit, Gradio, basic React/Next.js. * Testing & Guardrails: pytest, JUnit, LangSmith evaluation, NeMo Guardrails. * DevOps & Cloud: Docker, Google Cloud Platform (Vertex AI, Cloud Run, GKE), Git. Required Certifications: * Professional certifications specific to AI/ML (e.g., Certified AI Professional / CAIP, Google Cloud ML Engineer) considered a plus. Interview * Number of Interviews: 1, * Web Conference (Zoom/ TEAMs) ## Related Videos - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [How Unit Testing Saved My Career](https://www.wearedevelopers.com/videos/1642-how-unit-testing-saved-my-career) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) - [From boy scouting to redrawing the landscape](https://www.wearedevelopers.com/videos/1140-from-boy-scouting-to-redrawing-the-landscape) ## Related Articles - [Got AI ideas but no money? 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