Job Description:
. Relevant Experience & STEM Foundation: + years of professional software/ML engineering experience, with a dedicated AI/ML focus in the last – 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 (, 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.
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 clients 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 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 (, NeMo Guardrails) for medical device context. Track token usage, latency, and costs using LangSmith or Vertex AI monitoring.
Additional Preferred Skills (not required, but a plus):
Direct experience designing and deploying high-throughput REST APIs (, FastAPI/Flask).
Familiarity with medical device development regulations and compliance (, FDA guidelines, ISO ).
Experience integrating multiple LLM APIs beyond a single vendor (, OpenAI, AWS Bedrock, Anthropic Claude).
Front-end development/integration experience for UI design (, Streamlit, Gradio, React/ integration).
“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”