Description :

Role Overview :

We are looking for a Lead AI Data Engineer / Senior AI-ML Engineer to design, build, and deploy enterprise-grade AI and Generative AI solutions across AWS and Azure cloud platforms.

This role requires a strong mix of hands-on engineering, AI/ML architecture, and production deployment experience, with a focus on LLMs, RAG pipelines, AI agents, and cloud-native AI workflows. You will work closely with business and engineering stakeholders to translate complex problems into scalable, secure, and measurable AI solutions.

Key Responsibilities :

AI & GenAI Engineering :

- Design and implement end-to-end AI/ML and Generative AI solutions in cloud environments

- Build and deploy RAG (Retrieval-Augmented Generation) pipelines, including :

a. Prompt engineering

b. Vector database design

c. Embedding strategies

- Develop AI workflows and agent-based systems using modern orchestration frameworks

- Integrate LLMs into enterprise applications with focus on performance, reliability, and safety

Cloud Platforms & Architecture :

- Architect and deploy AI solutions using :

a. AWS : Amazon Bedrock, SageMaker, Lambda, DynamoDB, OpenSearch

b. Azure : Azure OpenAI, Azure ML, Azure Cognitive Services, Cognitive Search

- Implement serverless and containerized AI workloads using ECS / AKS

- Ensure scalability, fault tolerance, and cost efficiency of AI systems

MLOps, Monitoring & Governance :

- Implement model monitoring, logging, and performance tracking in production

- Apply model evaluation, fine-tuning strategies, and inference optimization

- Address AI safety, risk management, and compliance requirements

- Ensure strong cloud security, IAM, RBAC, and enterprise governance standards

Engineering & Collaboration :

- Write clean, production-ready Python code using modern ML libraries and APIs

- Integrate AI systems into CI/CD pipelines

- Collaborate with data, backend, and platform teams

- Translate business objectives into measurable AI outcomes

Required Skills & Experience :

Core Experience :

- 7 to 9 years of overall engineering experience

- 3 to 5 years of hands-on experience building AI / ML solutions

- Strong background in software and data architecture with AI/ML systems

AI & GenAI Expertise :

- Hands-on experience with LLMs in cloud environments

- Strong understanding of :

a. RAG architectures

b. Prompt engineering techniques

c. Vector databases

- Familiarity with AI agent orchestration frameworks (e.g., LangChain, Semantic Kernel, AWS Agent Framework)