Manager - BIM

Job description

**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 AIbased 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 - 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**. 1. **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**. 1. **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. 1. **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 AIbased 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**