Solution Architecture :

- Design and oversee secure, scalable enterprise AI/ML and Generative AI (LLM/SLM) architectures.

- Define solution architecture for AI-powered applications and enterprise workflows.

- Design architectures covering RAG, LLMs, agentic AI, intelligent automation, and AI assistants.

- Ensure solutions meet requirements around security, scalability, reliability, performance, and cost.

AI Strategy & Governance :

- Define AI reference architectures, technology roadmaps, and engineering standards.

- Establish AI governance frameworks covering Responsible AI, model deployment, security, and compliance.

- Evaluate emerging AI technologies and identify opportunities for enterprise adoption.

GenAI & Technology Integration :

- Architect and implement use cases such as :

1. Intelligent chatbots

2. Automated document processing

3. RAG applications

4. Agentic workflows

5. Enterprise AI assistants

- Work with LLMs, SLMs, vector databases, embeddings, and semantic search.

- Integrate modern GenAI orchestration frameworks into enterprise solutions.

Technical Leadership :

- Establish and contribute to AI Centers of Excellence (CoE).

- Mentor and guide AI Engineers, ML Engineers, Data Scientists, Architects, and MLOps teams.

- Conduct architecture and technical design reviews.

- Define best practices for AI engineering, deployment, monitoring, and optimization.

Consulting & Pre-Sales :

- Lead AI discovery and solutioning workshops with customers.

- Support RFP/RFI responses and technical solution proposals.

- Develop AI transformation roadmaps aligned with business objectives.

- Present AI architecture and transformation strategies to senior stakeholders.

Mandatory Skills :

AI / ML :

- Machine Learning & Deep Learning

- Generative AI / LLMs / SLMs

- RAG

- AI/ML Solution Architecture

- TensorFlow / PyTorch

GenAI Frameworks :

- LangChain

- LangGraph

- Semantic Kernel

- Agentic AI

Cloud & MLOps :

- Azure and/or AWS

- Cloud-native AI services

- Docker / Kubernetes

- CI/CD pipelines

- MLOps / LLMOps

- Model deployment and monitoring

Good to Have :

- Vector databases

- Knowledge Graphs

- Semantic Search

- Prompt Engineering

- LLM Evaluation

- Responsible AI & AI Governance

- Azure OpenAI / Amazon Bedrock

- Enterprise AI security

- Experience building reusable AI accelerators and frameworks

Preferred Candidate Profile :

- 5 - 10 years of overall IT experience with significant hands-on AI/ML architecture experience.

- Proven experience designing and delivering enterprise-scale AI/GenAI solutions.

- Strong understanding of cloud architecture and MLOps.

- Experience leading technical teams and mentoring AI/ML professionals.

- Strong consulting, communication, stakeholder-management, and presentation skills.

- Ability to translate business requirements into scalable AI solutions.