Sr. Data Scientist, AI Engineering

Trinity Life Sciences · Bangalore

Source and collection record

Last observed by our collector: 8 October 2026 at 10:56 pm IST. This is not a manual review or a confirmation from the employer.

Employer requisition: JR100429

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Collected role description

We're committed to bringing passion and customer focus to the business. Key Responsibilities: • Architect and implement RAG pipelines, agentic AI systems, and LLM-driven applications for enterprise use cases. • Design and integrate prompt engineering, context management, and knowledge-grounding frameworks to optimize LLM performance. • Collaborate with data, ML, and software engineering teams to build production-grade GenAI microservices and APIs. • Evaluate and integrate open-source and proprietary LLMs (e.g., OpenAI, Anthropic, Mistral, Llama, Gemini). • Design data pipelines for unstructured/structured content ingestion, indexing, and vector retrieval using Milvus, PostgreSQL (pgvector), or similar technologies. • Define and enforce architecture standards, governance, and best practices for scalable GenAI platforms. • Conduct PoCs, benchmark model performance, and lead solution transitions from prototype to production. • Contribute to AI strategy, model lifecycle management, and cost optimization initiatives. Required Skills and Qualifications: • Bachelor’s/Master’s degree in Computer Science, AI, Data Engineering, or related field. • 5–8 years of total experience, with at least 3+ years in AI/ML or NLP systems design. • Proven experience implementing LLM-based solutions, RAG architectures, and prompt orchestration frameworks. • Strong Python programming skills and familiarity with frameworks like LangChain, LangGraph, LlamaIndex, or Transformers. • Hands-on knowledge of vector databases (Milvus, Pinecone, Weaviate, Chroma) and knowledge graph systems (Neo4j, RDF). • Experience deploying and managing AI workloads on cloud platforms (GCP, Azure, AWS). • Understanding of MLOps/GenAIOps, model evaluation, and observability practices. • Strong problem-solving, communication, and stakeholder management capabilities. Preferred Skills • Experience with multimodal LLMs, agentic reasoning, and tool-using AI agents. • Exposure to pharma/life sciences or regulated enterprise domains. • Contribution to open-source AI frameworks or internal AI accelerators.

Collected experience requirement

5-8 years