Director

AXTRIA; Dadri; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

AXTRIA is seeking a highly skilled GenAI Application Leader to drive design, development, testing, and deployment of Generative AI applications focused on Life Sciences data analytics. You will lead cross-functional teams, collaborate with pharma experts, and guide architecture decisions across data platforms and enterprise BI integrations.

The role bridges AI engineering, data analytics, and full-stack development, enabling scalable, secure GenAI workflows and context-aware knowledge bases

Qualifications

• Strong background in Generative AI and data analytics in Life Sciences domain.
• Experience architecting end-to-end GenAI applications and APIs.
• Hands-on with LLMs, RAG, and knowledge graphs; cloud integrations.

Responsibilities

• Lead the end-to-end architecture and design of GenAI applications.
• Drive solution and architecture discussions with client teams using LLMs, RAG, and knowledge graphs.
• Guide team to build modular AI workflows with scalability, security, and performance.
• Evaluate and recommend GenAI frameworks/tools (LangChain, LangGraph, Semantic Kernel).
• Collaborate with pharma data experts to design semantic data models and context-aware knowledge bases.
• Drive full-stack development using Python (FastAPI, Flask) and React/Next.js for GenAI-powered frontends.
• Drive teams building microservices or API layers exposing AI functionalities securely.
• Design user-centric applications embedding GenAI outputs into Power BI and other BI tools.
• Connect GenAI apps to data ecosystems (AWS S3, Azure Data Lake, Snowflake, Databricks).
• Use knowledge graphs and metadata-driven approaches for contextual reasoning and data discovery.
• Integrate LLMs into enterprise-grade applications; fine-tune models on domain data.
• Design and implement RAG architectures with vector stores (ChromaDB, Pinecone, FAISS, Weaviate).
• Develop prompt engineering frameworks and guardrails for factuality and compliance.
• Establish evaluation pipelines for model performance, latency, and hallucination detection.
• Drive Gen AI use cases across BIM practice to speed and aid data analytics.
• Lead cross-functional GenAI teams of engineers, data scientists, and UI developers.
• Stay ahead with emerging LLM architectures and multi-agent systems.

Skills

Python

LangChain

LangGraph

RAG

Knowledge Graphs

LLM Frameworks

AWS/Azure

Fine-tuning

FastAPI

React

Next.js

Data Analytics

Education

BE/B.Tech

MCA

Tools

AWS

Azure

Snowflake

Databricks

ChromaDB

Pinecone

FAISS

Weaviate

Job description

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

• 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.
• Drive full-stack design and development using Python (FastAPI, Flask) and React/Next.js 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
• 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.
• 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.

Attention to P&L Impact

Lifescience Knowledge

Cultural Fit

Problem solving

Talent Management

Capability Building / Thought Leadership

Delivery Management- BIM/ Cloud Info Management