Manager

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