Backend AI Engineer

Posted 29d ago

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Craft and refine effective prompts for RAG, grounding, and context tuning
Develop asynchronous microservices (FastAPI) using SSE or WebSockets to stream real-time LLM responses
Design, develop, and implement robust Vector Databases using LLMs and modern retrieval technologies
Build and optimize pipelines to extract and structure multi-modal data from unstructured documents
Fine-tune and train generative AI models using engineering data and domain knowledge
Design and implement scalable backend APIs (FastAPI/REST) and UI integration interfaces
Develop backend functionalities to automatically generate technical requirements from design documents and user stories
Thoroughly document architecture, code, REST endpoints, and model training procedures
Partner closely with SMEs, System Engineers, and V&V Test teams to optimize AI-powered workflows
Implement hallucination checks, PII masking, and guardrails for medical device context, and track token usage, latency, and costs

Bachelor's degree in Software/Computer/IT/Systems/Biomedical Engineering or related technical discipline
4+ years of professional software/ML engineering experience with dedicated AI/ML focus in last 1-2 years
Hands-on experience with Gemini model family and Vertex AI including deployment, grounding, and integration
Experience with containerization (Docker) and deploying services via Cloud Run or GKE (Kubernetes)
Proficiency in Python and modern ML/AI frameworks (PyTorch, LangChain, LangSmith)
Proven experience building AI/LLM agents and tool-calling systems in Python against unstructured, multi-source data
Expertise in RAG pipelines, prompt engineering, context tuning, grounding, and Vector Databases (e.g., Milvus, Postgres/Pgvector)
Demonstrated ability to ingest, clean, extract, and structure text, tables, and images from unstructured documents (PDFs, design docs, etc)
Software skills: Python (AsyncIO, OOP), SQL, PyTorch, LangChain, LangSmith, Vertex AI SDK, FastAPI, Flask, REST APIs, SSE, Milvus, Pgvector, Docker, GCP (Vertex AI, Cloud Run, GKE), Git

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)

Skills