Job Description :

Role & Responsibilities :

- Define and implement enterprise-wide AI architecture leveraging Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent systems.

- Design scalable, secure, and cloud-native AI platforms on Azure and AWS ecosystems.

- Build advanced knowledge retrieval solutions using vector databases, knowledge graphs, semantic search, and hybrid retrieval techniques.

- Lead development and deployment of production-grade AI applications with MLOps, observability, governance, and continuous evaluation frameworks.

- Establish reusable AI reference architectures, engineering standards, and best practices across product teams.

- Drive AI platform strategy including model selection, prompt engineering, fine-tuning, performance optimization, and cost management.

- Ensure Responsible AI practices covering security, privacy, compliance, explainability, and risk mitigation.

- Collaborate with business stakeholders, product teams, architects, and engineering leaders to translate business challenges into AI-driven solutions.

- Mentor engineering teams and promote enterprise-wide AI adoption, innovation, and transformation initiatives.

- Provide technical leadership for AI roadmap planning, architecture reviews, and strategic technology decisions.

Preferred Candidate Profile :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.

- 10+ years of experience in software engineering, solution architecture, or enterprise technology platforms, with significant experience in AI/ML and Generative AI.

- Strong expertise in LLMs, RAG architectures, prompt engineering, agentic AI frameworks, and enterprise AI solution design.

- Hands-on experience with Azure OpenAI, AWS Bedrock, cloud-native architectures, microservices, APIs, and Kubernetes.

- Experience with vector databases, knowledge graphs, semantic search, and enterprise data platforms.

- Strong understanding of MLOps, AI observability, model evaluation, monitoring, and governance frameworks.

- Knowledge of Responsible AI, data privacy, security, compliance, and enterprise risk management practices.

- Proven ability to lead cross-functional teams, mentor engineers, and influence stakeholders at leadership levels.

- Excellent communication, problem-solving, and strategic thinking skills.

- Experience in manufacturing, industrial automation, digital engineering, or enterprise software domains is highly desirable.