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About the role
Position Summary Highly skilled GenAI Application Leads with 8 to 15 years of total experience who can lead the design, development, testing, and deployment of Generative AI–based applications focused on Data and Analytics in Life Sciences domain . The ideal candidate will have a strong background in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph), AWS/Azure cloud services with hands-on experience integrating and fine-tuning GPT, Anthropic Claude, Mistral, or Snowflake Cortex for real-world business use cases. Strong client problem-solving skills across life sciences data and analytics is a plus.
This role bridges AI engineering, data analytics, and full-stack development, creating intelligent applications that augment data-driven decision-making.
Job Responsibilities
Solution Architecture & Design
Lead the end-to-end architecture and design of Generative AI applications
Define solution blueprints combining LLMs, Retrieval-Augmented Generation (RAG), Knowledge Graphs for structured and unstructured data sources.
Translate business requirements into modular AI workflows , ensuring scalability, security, and performance.
Evaluate and recommend GenAI frameworks/tools (LangChain, LangGraph, Semantic Kernel, etc.)
Collaborate with data engineers and pharma domain experts to design semantic data models and context-aware knowledge base .
Gen AI Application Development & Engineering
Lead full-stack design and development using Python ( FastAPI , Flask) and React /Next.js for GenAI-powered frontends.
Build microservices or API layers that expose AI functionalities securely across teams and systems.
Ensure robust CI/CD pipelines, version control (GitHub, Bitbucket, GitLab), and containerization (Docker, Kubernetes).
Design and develop user-centric applications that embed GenAI outputs seamlessly into custom UI or enterprise BI tools like Power BI
Work with data engineering and analytics teams to connect GenAI apps to existing data ecosystems (AWS S3, Azure Data Lake, Snowflake, Databricks, etc.)
Use knowledge graphs and metadata-driven approaches to enhance contextual reasoning and data discovery
Deploy AI workloads using Azure OpenAI, AWS Sagemaker, Bedrock, or Snowflake Cortex AI Services .
AI Model Integration & Fine-tuning
Lead the integration of LLMs (OpenAI GPT, Anthropic Claude, Mistral, Snowflake Cortex, etc.) into enterprise-grade applications.
Fine-tune or prompt-tune foundation models using domain-specific data (commercial, patient, Omni -channel, clinical, or market access data).
Design and implement RAG architectures leveraging vector databases (ChromaDB, Pinecone, FAISS, Weaviate etc.).
Develop prompt engineering frameworks and guardrails to ensure factuality, interpretability, and compliance.
Establish evaluation pipelines for model performance, accuracy, latency, and hallucination detection.
Leadership & Collaboration
Lead a cross-functional GenAI development team of engineers, business analysts, data scientists, and UI developers.
Stay ahead of the curve with emerging LLM architectures, multi-agent systems, and reasoning frameworks to provide technical guidance to the teams.
Drive knowledge-sharing sessions and PoCs to evangelize Generative AI adoption across the organization.
Contribute to Gen AI use case roadmaps, thought leadership relevant to GenAI in Life Sciences.
Education BE/B.Tech Master of Computer Application Work Experience Highly skilled GenAI Application Leads with 8 to 15 years of total experience who can lead the design, development, testing, and deployment of Generative AI–based applications focused on Data and Analytics in Life Sciences domain . The ideal candidate will have a strong background in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph), AWS/Azure cloud services with hands-on experience integrating and fine-tuning GPT, Anthropic Claude, Mistral, or Snowflake Cortex for real-world business use cases. Strong client problem-solving skills across life sciences data and analytics is a plus. Behavioural Competencies Teamwork & Leadership Motivation to Learn and Grow Ownership Cultural Fit Talent Management Technical Competencies Problem Solving Lifescience Knowledge Communication Project Management Capability Building / Thought Leadership AIML Python React Azure ML Studio AWS CodeBuild ML Data Science Snowflake Skills
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