Generative AI Engineer

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)

• 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.

DivIHN, the ‘‘IT Asset Performance Services’’ organization, provides Professional Consulting, Custom Projects, and Professional Resource Augmentation services to clients in the Mid-West and beyond. The strategic characteristics of the organization are Standardization, Specialization, and Collaboration.