Sr. Data Scientist, AI Engineering

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.