Job Overview :

We are looking for an experienced AI Lead / Architect with strong expertise in Enterprise Generative AI, Agentic AI, and AWS cloud technologies. The ideal candidate should have hands-on experience designing, architecting, and implementing scalable AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent architectures.

The candidate will be responsible for owning end-to-end AI architecture, from requirement discovery and proof of concept to production deployment. This role also involves building reusable AI frameworks, defining architectural best practices, and providing technical leadership to engineering teams to deliver secure, scalable, and enterprise-ready AI solutions.

Key Responsibilities :

- Lead the architecture, design, and implementation of enterprise Generative AI and Agentic AI solutions on AWS.

- Own the complete AI solution lifecycle, from requirement discovery and proof of concept to production deployment.

- Design and develop scalable LLM-based applications, RAG pipelines, and multi-agent architectures.

- Build reusable frameworks and components for LLM integration, agent orchestration, and AI-powered workflows.

- Leverage AWS AI services, particularly Amazon Bedrock and Amazon SageMaker, to develop and deploy AI solutions.

- Implement prompt engineering, embeddings, semantic search, and vector database integrations.

- Design and develop REST APIs, microservices, and event-driven architectures for AI-powered applications.

- Architect cloud-native solutions using serverless technologies, containers, and Kubernetes.

- Integrate external tools and systems using MCP (Model Context Protocol) or similar frameworks.

- Establish AI security standards, guardrails, Responsible AI practices, and governance frameworks.

- Implement AI model evaluation, observability, monitoring, and performance optimization.

- Ensure AI solutions meet enterprise requirements for scalability, reliability, security, and cost efficiency.

- Provide technical leadership, mentor engineering teams, and drive architectural decisions.

- Collaborate with business stakeholders and cross-functional teams to translate business requirements into effective AI solutions.

Mandatory Skills & Experience :

- 8 - 12 years of overall experience in software engineering, AI/ML, or solution architecture.

- Strong hands-on experience designing and implementing enterprise AI solutions.

- Expertise in AWS AI services, particularly :

1. Amazon Bedrock

2. Amazon SageMaker

- Strong understanding of Generative AI concepts, including :

1. Large Language Models (LLMs)

2. Retrieval-Augmented Generation (RAG)

3. Multi-agent architecture

4. Agent orchestration

- Hands-on experience with AI engineering concepts such as :

1. Prompt Engineering

2. Embeddings

3. Semantic Search

4. Vector Databases

- Strong programming experience in Python and/or TypeScript.

- Experience developing REST APIs, microservices, and event-driven systems.

- Good understanding of serverless architecture, containers, and Kubernetes.

- Experience integrating tools and external systems using MCP or similar frameworks.

- Strong knowledge of production AI practices, including :

1. AI Security and Guardrails

2. Responsible AI Governance

3. Model Evaluation

4. Observability and Monitoring

- Excellent technical leadership, communication, and stakeholder management skills.