Experience

- 10+ years overall experience in software engineering, data, AI/ML, or architecture
- 5+ years designing and delivering AI/ML and Generative AI solutions
- Experience leading enterprise-scale AI programs

Role Summary

Lead the architecture, governance, and delivery of enterprise AI solutions spanning Machine Learning, Predictive Analytics, Deep Learning, Generative AI, and Agentic AI. Define scalable, secure, and business-aligned AI platforms, applications, and standards.

Key Responsibilities

AI Architecture

- Design end-to-end AI/ML and GenAI solution architectures.
- Define AI reference architectures, standards, and best practices.
- Architect AI-powered applications using ML models, LLMs, RAG, copilots, and AI agents.

AI Platforms & Engineering

- Design AI platforms supporting MLOps, LLMOps, model deployment, monitoring, and governance.
- Establish reusable AI services, APIs, accelerators, and frameworks.
- Drive scalability, reliability, security, and cost optimization.

Data & Cloud

- Architect cloud-native AI solutions on AWS, Azure, or GCP.




- Design data pipelines, lakehouse, feature store, and vector database architectures.
- Integrate AI solutions with enterprise systems and business applications.

Governance & Leadership

- Define Responsible AI, security, compliance, and governance frameworks.
- Lead architecture reviews and mentor AI engineering teams.
- Partner with business stakeholders to identify and prioritize AI opportunities.

Required Skills

- Machine Learning, Deep Learning, NLP, Predictive Analytics
- LLMs, RAG, AI Agents, Prompt Engineering, Vector Databases
- Python, TensorFlow/PyTorch, LangChain/LangGraph, MLflow
- MLOps, LLMOps, Docker, Kubernetes, CI/CD
- AWS, Azure, or GCP

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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