AI Engineer

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.