Note: The job is a remote job and is open to candidates in USA. DivIHN Integration Inc is seeking a Senior Generative AI Engineer to develop and deploy production-grade AI solutions using Google Gemini and Vertex AI. The role focuses on RAG and prompt engineering, asynchronous APIs, vector databases, multimodal data pipelines, LLM fine-tuning, AI agents, and guardrails, while collaborating with engineering and testing teams.
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
Skills
- • 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)
- • Minimum Bachelors in Software/Computer/IT/Systems/Biomedical Engineering + 3 years
- • Languages: Python (AsyncIO, OOP), SQL
- • 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, GCP (Vertex AI, Cloud Run, GKE), Git
- 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)
- • 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)
- • Professional certifications specific to AI/ML (e.g., Certified AI Professional / CAIP, Google Cloud ML Engineer) considered a plus
Benefits
- Remote work arrangement
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