Project Leader (10751)

Highly skilled Gen AI Engineering Leads with 4 to 7 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 hands-on experience in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph, LangFuse), Observability, Guardrails and prompt tuning. Experience with LLM Models like Azure Open AI, Anthropic Claude or fully managed AI services like Snowflake Cortex or Databricks Genie. Good experience working with AWS /Azure cloud services and AI platforms like AWS Bedrock Agent Core and Azure Foundry 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.

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. 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.