We are seeking a Lead AI Transformation & GenAI Engineer to drive enterprise AI initiatives from use-case identification and solution architecture through development, production deployment, adoption, and value realization. The successful candidate will combine strong hands-on expertise in Machine Learning, Generative AI, Agentic AI, LLMs, cloud platforms, and intelligent automation with stakeholder-facing consulting and technical leadership skills. The role requires the ability to independently lead multiple AI initiatives, mentor junior team members, and translate Life Sciences and pharmaceutical business challenges into secure, scalable, and responsible AI solutions.

Key Responsibilities :

- Lead AI transformation programs by identifying high-value opportunities and converting business challenges into practical AI use cases and delivery roadmaps.

- Define AI strategy, target architecture, technology choices, implementation plans, success measures, and adoption approaches for enterprise AI initiatives.

- Design, develop, and deploy Generative AI, Agentic AI, Machine Learning, and intelligent automation solutions.

- Build LLM-powered applications using GPT, Claude, Gemini, Azure OpenAI, and related technologies.

- Develop RAG pipelines, AI agents, multi-agent systems, prompt engineering solutions, LLM fine-tuning workflows, evaluation frameworks, and guardrails.

- Build agentic workflows using LangGraph, LangChain, CrewAI, AutoGen, and Semantic Kernel.

- Develop enterprise solutions using Copilot Studio, Microsoft 365 Copilot Extensions, Power Apps, Power Automate, Dataverse, Azure AI Services, and Azure OpenAI.

- Build APIs and AI applications using Python, SQL, REST APIs, FastAPI, Flask, and Streamlit.

- Work with Snowflake, Azure, Databricks, AWS/GCP, vector databases, data pipelines, and data engineering workflows.

- Implement Responsible AI, AI governance, security, observability, model monitoring, evaluation, and risk-control practices.

- Take solutions from ideation and proof of concept through production deployment, operationalization, adoption, and continuous improvement.

- Collaborate with business, data, engineering, security, and leadership stakeholders; mentor junior team members and provide technical direction across parallel projects.

- Establish end-to-end ML workflows covering data preparation, experimentation, model deployment, monitoring, retraining, and integration with enterprise applications.

- Perform feature engineering, model selection, hyperparameter tuning, performance evaluation, explainability, and error analysis using appropriate statistical and ML techniques.

- Design, train, validate, and optimize machine learning models for classification, regression, clustering, forecasting, NLP, and predictive analytics use cases.

Qualifications :

- Education : Bachelor's or Master's degree in Life Sciences, Data Analytics, Economics, or a related field.

Preferred Skills & Technologies :

- Machine Learning & AI : Supervised/Unsupervised Learning, NLP, Deep Learning, Time Series & Predictive Modeling, Explainable AI (XAI).

- Generative AI & Agentic AI : Hands-on experience on working with LLMs (GPT, Claude, Gemini) Azure OpenAI, Fine-Tuning LLMs, Prompt Engineering, RAG (Retrieval-Augmented Generation), Agentic AI Frameworks (LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel), AI Agents & Multi-Agent Systems, LLM Evaluation & Guardrails.

- Microsoft AI & Automation Stack : Copilot Studio, Microsoft 365 Copilot Extensions, Power Apps / Power Automate / Dataverse / Azure AI Services / Azure OpenAI.

- Programming & Engineering : Python, SQL, REST APIs, FastAPI / Flask, Git & CI/CD, Workflow Automation.

- Cloud & Data Platforms : Snowflake, Azure (preferred), AWS / GCP / Databricks, Vector Databases, Data Pipelines & Data Engineering.

- Life Sciences Domain : Commercial Pharma Analytics, Market Access & Payer Analytics, Specialty Pharmacy, Patient Analytics, Real World Data (RWD) / Claims Analytics.

Good to Have :