Role Summary 

We are seeking a highly skilled Senior Consultant - AI Engineer to design, develop, and deploy scalable AI-powered solutions that address complex commercial, medical, and patient-centric challenges in the Healthcare and Life Sciences industry. This role combines advanced AI engineering, machine learning, Generative AI, Agentic AI, and healthcare analytics expertise to create innovative, production-grade solutions that drive measurable business value. 

The ideal candidate will have strong experience in building enterprise AI applications, LLM-based solutions, intelligent agents, and modern analytics platforms while working with healthcare datasets such as claims, prescription, EMR/EHR, real-world data (RWD), CRM, patient support, clinical, and scientific data. 

This individual will partner closely with business stakeholders, methodologists, data scientists, and technology teams to translate business problems into scalable AI solutions, reusable accelerators, and intellectual property. 

Key Responsibilities 

AI Solution Development & Engineering 

• Design, build, deploy, and maintain production-grade AI and machine learning solutions across commercial, medical, and patient analytics use cases. 

• Develop enterprise-scale Generative AI and Agentic AI applications leveraging Large Language Models (LLMs). 

• Build Retrieval-Augmented Generation (RAG) architectures, semantic search systems, knowledge agents, and intelligent workflow automation solutions. 

• Create reusable AI frameworks, APIs, accelerators, and platform components that can be deployed across multiple clients and therapeutic areas. 

• Develop scalable AI orchestration frameworks using agent-based architectures and workflow management tools. 

AI Platform & MLOps Engineering 

• Develop cloud-native AI solutions using modern AI development platforms and frameworks. 

• Implement software engineering best practices including CI/CD, version control, automated testing, observability, and monitoring. 

• Establish AI deployment pipelines for model operationalization, monitoring, governance, and performance optimization. 

• Ensure scalability, security, reliability, and compliance of AI solutions in regulated healthcare environments. 

• Collaborate with data engineering teams to build robust data pipelines and AI-ready data ecosystems. 

Generative AI & Agentic AI 

• Design and implement:  

• Large Language Model (LLM) applications 

• Retrieval-Augmented Generation (RAG) systems 

• Semantic search solutions 

• Prompt engineering frameworks 

• AI agents and multi-agent systems 

• Workflow orchestration platforms 

• Evaluate, validate, and benchmark AI models to ensure accuracy, reliability, explainability, and business relevance. 

• Contribute to AI governance, Responsible AI practices, and model risk management frameworks. 

Healthcare Analytics & Business Impact 

• Apply AI and advanced analytics to solve Healthcare and Life Sciences challenges using:  

• Claims data 

• Prescription data 

• EMR/EHR data 

• Real-World Data (RWD) 

• Commercial and CRM data 

• Patient support data 

• Clinical and scientific data 

• Develop AI-enabled solutions that improve commercial effectiveness, customer engagement, forecasting accuracy, patient outcomes, and decision support. 

• Partner with healthcare domain experts to ensure solutions align with industry needs and regulatory expectations. 

Stakeholder Management & Consulting 

• Translate ambiguous business challenges into structured AI and analytics solutions. 

• Engage with business leaders, clients, methodologists, and cross-functional teams to define solution roadmaps and implementation strategies. 

• Present technical concepts and AI insights in a manner that is understandable to executive and non-technical audiences. 

• Mentor junior engineers and contribute to building organizational AI capabilities and best practices. 

• Support thought leadership initiatives and contribute to AI innovation within the organization. 

Must-have requirements:

• Hands-on experience with Generative AI, LLMs, and RAG
• Experience with Agentic AI, semantic search, and AI workflow orchestration
• Strong Python and SQL skills
• Experience with Databricks, Snowflake, Spark/PySpark, and cloud platforms (Azure, AWS, or GCP)
• Experience building APIs, scalable AI services, and enterprise AI applications
• Knowledge of MLOps/LLMOps, CI/CD, Docker/Kubernetes, Git, and model deployment
• Healthcare/Life Sciences experience, including claims, EMR/EHR, RWD, prescription, clinical, or CRM data
• Strong stakeholder management, consulting, and communication skills
• Ability to translate business problems into scalable AI solutions and mentor junior team members