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Responsibilities:
Design and develop production-grade AI-powered backend systems for enterprise life sciences and genomics applications.
Build and own Agentic AI workflows, autonomous agents, multi-agent orchestration, and tool-calling pipelines.
Develop and optimise RAG pipelines for intelligent document and scientific data processing.
Integrate Large Language Models into scalable healthcare and genomics applications.
Build robust REST APIs and backend services using Python to support AI inference and enterprise workflows.
Work with large-scale structured and unstructured datasets: genomics data, clinical records, multi-omics datasets.
Contribute to MLOps practices: model deployment, monitoring, and lifecycle management.
Collaborate with scientific domain teams, product managers, and cross-functional engineers to translate requirements into scalable AI solutions.
Ensure systems are built with regulatory compliance and data security standards appropriate for healthcare AI.
Participate in technical design reviews and contribute to platform architecture decisions.
Requirements:
4-8 years of experience in AI Engineering, Backend Development, or ML Engineering.
Strong hands-on Python.
Agentic AI experience (Mandatory): hands-on building and deploying autonomous AI agents in production.
Experience with agentic frameworks LangGraph, LangChain, LlamaIndex, CrewAI, or AutoGen.
Deep understanding of RAG architectures, vector DBs, embeddings, and document pipelines.
Experience with LLM APIs: OpenAI, Anthropic, Gemini, or similar.
Hands-on with Vector Databases: Pinecone, Weaviate, Chroma, or pgvector.
Strong experience building REST APIs and backend services with FastAPI, Flask, or Django.
Experience with cloud platforms AWS, GCP, or Azure.
Strong understanding of data pipelines and ETL processes for large-scale datasets.
Good to Have:
Exposure to genomics, bioinformatics, NGS, or clinical data domains.
Familiarity with bioinformatics tools and pipelines: GATK, BWA, or similar.
Experience with MLOps and LLMOps frameworks.
Knowledge of healthcare data standards: HL7 FHIR or DICOM.
Familiarity with Docker, Kubernetes, or CI/CD pipelines.
Experience working in regulated industries: healthcare, pharma, or life sciences.
Background in data engineering or distributed systems.
Experience
5-8 yrs
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