Director - BIM

Position Summary

Highly skilled GenAI Application Leader with 15+ years of total experience who can drive 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

1. Solution Architecture & Design

• Lead the end-to-end architecture and design of Generative AI applications
• Drive solution and architecture discussions with client teams using LLMs, Retrieval-Augmented Generation (RAG), Knowledge Graphs for structured and unstructured data sources.
• Understand business requirements and guide team to build the same 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.

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2. Leading Gen AI Application Development & Engineering Teams

• Drive full-stack design and development using Python (FastAPI, Flask) and for GenAI-powered frontends.
• Drive teams building microservices or API layers that expose AI functionalities securely across teams and systems.
• Design 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.)
• Drive usage of knowledge graphs and metadata-driven approaches to enhance contextual reasoning and data discovery

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2. AI Model Integration & Fine-tuning

• Drive 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.

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2. Leadership & Collaboration

• Drive Gen AI use cases which are relevant to BIM practice (focused on data management and analytics)
• Drive adoption of Gen AI use cases across BIM practice to drive speed and efficiency gains
• 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.

Education BE/B.Tech Master of Computer Application Work Experience

Highly skilled GenAI Application Leader with 15+ years of total experience who can drive 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.

Behavioural Competencies Attention to P&L Impact Teamwork & Leadership Lifescience Knowledge Cultural Fit Problem solving Talent Management Capability Building / Thought Leadership Delivery Management- BIM/ Cloud Info Management Technical Competencies AWS CodeBuild ML Data Science AIML Databricks Snowflake Azure ML Studio ML Ops ML engineering React Python Skills

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