Description

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.
• Collaborate closely with engineers and cross-functional teams to design and develop AI solutions.
• Develop and integrate Generative AI applications and APIs.
• Work with large datasets, document processing, and data pipelines.
• Participate in problem-solving, solution design, and technical discussions.
• Communicate effectively with team members and stakeholders.

Must-Have Qualifications :

•  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.

Role Information

• Generative AI Engineer role.
• Experience in API development.
• Hands-on experience with Google Gemini and Vertex AI.
• Knowledge of Agentic AI frameworks and architectures.
• Strong Python development skills, including experience with LangChain and related AI frameworks.
• Open to candidates from any industry domain.

Required Skills :

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 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).
4. Strong hands-on experience in Artificial Intelligence and Generative AI
5. Python programming expertise.
6. Experience with LangChain and Agentic AI frameworks.
7. Understanding data handling to pull data from PDFs, design docs, regulatory files

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.

Software Skills Required:

• 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.

Required Certifications:

• Professional certifications specific to AI/ML (e.g., Certified AI Professional / CAIP, Google Cloud ML Engineer) considered a plus

Key Skills

Education

Bachelor's degree