Assistant General Manager - AI & GenAI Engineering

Job Description:

Role Purpose:

We are looking for a hands-on AI Engineering Leader to build and lead the organization's AI & GenAI development capability.

The role will be responsible for designing, developing and deploying AI/GenAI solutions, building a strong AI engineering team, establishing development standards, and converting business opportunities into scalable AI solutions.

The ideal candidate should be technically strong, an experienced Python/AI developer, and capable of leading a team of AI Engineers, Data Engineers and AI/ML specialists.

Experience in Pharmaceutical / Life Sciences / Manufacturing will be preferred.

Key Responsibilities:

1. AI & GenAI Development Primary:

- Lead the development of enterprise AI, GenAI and Agentic AI solutions.

- Personally contribute to architecture, coding and solution development.

- Develop AI Agents, Copilots, RAG applications and intelligent assistants.

- Build AI solutions using Azure AI Foundry, Azure OpenAI and related Azure AI services.

- Develop Python-based AI applications, APIs and microservices.

- Integrate AI solutions with enterprise applications and data platforms.

- Build reusable AI frameworks, components and accelerators.

- Evaluate and implement emerging AI technologies.

2. AI Engineering Team Leadership:

- Build, manage and mentor the AI Engineering team.

- Define team structure, skills, roles and development roadmap.

- Guide engineers on architecture, coding, AI development and deployment practices.

- Conduct technical reviews and ensure engineering quality.

- Establish standards for Git, CI/CD, testing, documentation and production deployment.

- Develop internal AI capabilities and reduce dependency on external vendors.

- Create a culture of innovation, experimentation and rapid prototyping.

3. AI Solution Architecture:

- Define architecture for enterprise AI and GenAI solutions.

- Establish standards for:

1. RAG

2. AI Agents

3. LLM integration

4. Prompt engineering

5. Vector search

6. Model evaluation

7. AI security

8. MLOps

- Design scalable and production-ready AI solutions.

- Ensure AI solutions can integrate securely with enterprise systems.

4. AI Use Case Delivery:

- Work with business teams to identify high-value AI opportunities.

- Convert business requirements into AI solution designs and prototypes.

- Lead the complete lifecycle:

1. Idea PoC Prototype Production Scale

- Prioritize AI use cases based on business value, feasibility and data availability.

- Ensure successful adoption and measurable business outcomes.

5. Data Platform Enablement:

- Leverage Snowflake as a key enterprise data foundation for AI.

- Work with Data Engineering teams to ensure availability of high-quality data for AI solutions.

- Utilize Qlik and enterprise analytics as inputs to AI-driven insights and applications.

- Integrate AI solutions with SAP, Salesforce, ServiceNow, manufacturing systems and other enterprise platforms.

6. Stakeholder & Partner Management:

- Work closely with business leaders, IT teams and functional stakeholders.

- Translate business problems into practical AI solutions.

- Manage technology partners and AI development vendors where required.

- Present AI roadmap, solutions and progress to senior management.

Must-Have Technical Skills:

AI / GenAI:

- Generative AI / LLMs

- AI Agent / Agentic AI development

- RAG architecture

- Prompt Engineering

- Vector Databases

- LangChain / LangGraph or equivalent frameworks

- Machine Learning / Predictive Analytics

- AI evaluation and monitoring

Development:

- Python - Advanced

- REST APIs

- FastAPI / equivalent

- Git / GitHub

- CI/CD

- Docker / containerization

- Software engineering and coding best practices

Microsoft AI / Cloud:

- Azure AI Foundry

- Azure OpenAI

- Azure AI Services

- Azure Cloud

- Azure AI / ML services

Data:

- Snowflake

- SQL

- Data pipelines / ETL

- Data modelling

- API-based data integration

Analytics:

- Qlik Sense / Qlik

- Advanced Analytics

Preferred Experience:

- 10 - 15+ years of technology / software / data experience.

- 5+ years of hands-on AI/ML/GenAI development experience.

- Experience leading an AI / Data Science / Engineering team.

- Proven experience taking AI solutions from PoC to production.

- Experience building internal AI capabilities rather than only managing vendors.

- Pharmaceutical / Life Sciences / Manufacturing experience preferred