Description

Key Responsibilities

1. AI Solution Design & Architecture

• Design end-to-end AI, ML, and GenAI solution approaches aligned with business objectives.
• Translate complex pharmaceutical and business problems into AI-enabled solution designs.
• Define solution components such as data inputs, AI models, GenAI workflows, APIs, user interfaces, orchestration layers, and deployment patterns.
• Evaluate and recommend suitable AI models, LLMs, frameworks, cloud services, vector databases, and automation tools.
• Create solution design documents, technical blueprints, architecture diagrams, and implementation roadmaps.

2. GenAI & Applied AI Solutioning

• Design GenAI solutions using LLMs, RAG pipelines, prompt engineering, agents, knowledge search, summarization, automation, and workflow augmentation. Driving Innovation, Empowering Insights
• Identify opportunities where AI can improve productivity, decision-making, analytics, and business processes.
• Define build-vs-buy considerations for AI solutions and recommend appropriate technology stacks.
• Develop proof-of-concepts or prototypes to validate solution feasibility.
• Guide teams on responsible AI, explainability, model limitations, and risk mitigation.

3. Problem Framing & Business Translation

• • Work closely with business stakeholders to understand needs, pain points, workflows, and success criteria.
• Convert business requirements into AI use cases, functional specifications, and technical solution designs.
• Assess data availability, feasibility, complexity, risks, dependencies, and expected business impact.
• Prioritize AI use cases based on value, feasibility, scalability, and adoption potential.
• Communicate solution options, trade-offs, risks, and recommendations to both technical and non-technical audiences.

4. End-to-End Delivery Ownership

• Own the solution lifecycle from ideation to design, prototype, implementation support, deployment, and adoption.
• Partner with AI/ML engineers, data engineers, cloud architects, application teams, and platform teams to deliver production-ready solutions.
• Ensure solutions are scalable, secure, maintainable, and aligned with enterprise architecture standards.
• Support productionization through MLOps, LLMOps, monitoring, governance, and continuous improvement practices.
• Track business outcomes and ensure solutions deliver measurable impact.

5. Collaboration with Engineering & Platform Teams

• Provide technical direction to development and engineering teams during implementation.
• Collaborate on API design, data pipeline integration, model deployment, prompt management, vector search, and cloud architecture.
• Ensure AI solutions are designed for reliability, performance, security, privacy, and compliance.
• Review implementation approaches and help resolve design or integration challenges.
• Act as a bridge between business teams, AI teams, and technology delivery teams. Driving Innovation, Empowering Insights

6. Pharmaceutical Domain Application

• Apply AI and GenAI solutions across pharmaceutical domains such as:
• Clinical trials
• Real-world evidence
• Medical affairs
• Drug discovery
• Commercial analytics
• Patient analytics
• Regulatory and safety operations
• Sales and marketing effectiveness
• Understand pharma data types, workflows, compliance expectations, and business challenges.
• Ensure AI solutions consider regulatory, data privacy, security, and ethical AI requirements.
• Support use-case design for regulated and sensitive healthcare environments.

Technical Expertise Required Skills

• Strong understanding of AI, machine learning, GenAI, and applied analytics concepts.
• Ability to design AI solutions without being limited to hands-on model development.
• Experience with Python and SQL.
• Good understanding of ML models, statistical methods, deep learning, NLP, and LLM-based systems.
• Experience designing or working with:
• LLMs and GenAI platforms
• RAG architectures
• Vector databases
• Prompt engineering frameworks
• AI agents and workflow automation
• APIs and application integration patterns
• Cloud platforms such as AWS or Azure
• MLOps or LLMOps practices

Architecture & Solution Design Skills

• Proven ability to design end-to-end AI/ML/GenAI systems.
• Understanding of data pipelines, model serving, cloud deployment, orchestration, monitoring, and governance.
• Ability to create solution architecture diagrams, technical design documents, and implementation plans.
• Experience evaluating tools, models, platforms, and frameworks based on business and technical needs.
• Understanding of scalability, security, privacy, performance, and maintainability considerations.

Pharmaceutical Domain Knowledge

• Experience delivering AI, analytics, automation, or GenAI solutions in the pharmaceutical, healthcare, life sciences, or related industries.
• Familiarity with pharma datasets, business processes, and industry challenges.
• Understanding of compliance and privacy considerations such as GxP, HIPAA, GDPR, or similar frameworks is preferred.
• Ability to engage with domain stakeholders and convert domain problems into practical AI solution designs.

Experience Requirements

• 6–8 years of experience in AI, data science, analytics, solution design, or technology consulting roles.
• Demonstrated experience designing and delivering AI/ML or GenAI solutions.
• Experience working with business stakeholders to define use cases and solution approaches.
• Experience collaborating with engineering teams to implement and productionize AI solutions.
• Prior experience in pharma, healthcare, or life sciences is strongly preferred

Key Skills

Education

Any Gradute