
GenAI Engineer
3 days ago
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.
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