GenAI Engineer

Role Expectations:

• Drive innovation through state-of-the-art LLM integration and Retrieval-Augmented Generation (RAG) strategies using Dataiku and modern AI frameworks
• Experience with LLM Mesh, RAG pipelines, vector databases, embeddings, prompt engineering, and integration
• Build applications powered by LLMs (OpenAI, Claude, Mistral, etc.) using LangChain, LlamaIndex, and related GenAI frameworks.
• Implement RAG pipelines with vector DBs (Pinecone, FAISS, pgvector, ChromaDB) for grounding LLM responses with internal knowledge
• Develop multimodal AI solutions (text, audio, image) and build autonomous agents where relevant.
• Continuously improve model reliability, relevance by tuning prompt flows
• Deploy GenAI models on AWS/GCP/Azure using services like SageMaker, Bedrock, Vertex AI
• Ideate and design innovative solution frameworks aligning to market trends and technological innovations. Translating healthcare clients' business needs into technical solutions meeting user needs and business goals
• POC building, go to market strategy building by application of AI/Gen AI use cases wherever applicable. Integrate large language models (LLMs), computer vision, or other AI/ML techniques into business solutions.

Qualifications:

• Bachelors or Master’s degree in Computer Science, AI/ML, Data Science, or related field.
• 4-6 years of experience in software development, with at least 2 years in AI/ML or GenAI. Experience with LLM Mesh. (preferred - Dataiku LLM Mesh)
• Deep understanding of digital channels, marketing tactics, brand strategies, and marketing analytics
• Hands-on experience with LLMs (OpenAI, Claude, LLaMA, etc.), vector databases, and GenAI frameworks (LangChain, LlamaIndex, etc.).
• Proficiency in Python, pyspark and/or Java/JavaScript/TypeScript.
• Strong understanding of cloud platforms (Azure/AWS/GCP), especially AI-related services.
• Experience with frontend frameworks (React, Angular) and backend APIs.
• Pharma and Healthcare domain understanding is good to have.
• Proficient in Dataiku LLM mesh and RAG approaches.