We are looking for an experienced AWS AI Lead to lead the design, development, and delivery of AI, Machine Learning, and Generative AI solutions on AWS. The ideal candidate will have strong handson experience with Amazon Bedrock, Amazon SageMaker, RAG, Prompt Engineering, Python, and AI/ML model evaluation.

The role involves leading AI/ML implementation squads, translating business requirements into scalable AI solutions, reviewing technical architecture and solution quality, mentoring engineers, and ensuring that AI initiatives deliver measurable business outcomes. The candidate will work closely with Solution Architects, Data Engineers, Application Developers, Product Teams, and Business Stakeholders to drive AI/GenAI initiatives from ideation through production.

Key Responsibilities :

AI / ML / GenAI Leadership :

- Lead AI, ML, and GenAI implementation squads across multiple projects and use cases.

- Translate business challenges into clearly defined AI use cases, technical requirements, and implementation backlogs.

- Drive the endtoend delivery of AI solutions from proof of concept to production.

- Evaluate emerging GenAI capabilities and identify opportunities for business adoption.

- Define technical approaches, solution components, milestones, and delivery priorities.

AWS AI & GenAI Solutions :

- Design and implement AI/ML solutions using Amazon Bedrock, Amazon SageMaker, and AWS AI services.

- Develop and review GenAI architectures involving foundation models, embeddings, RAG pipelines, vector databases, and APIs.

- Build scalable and secure solutions leveraging AWSnative services.

- Integrate AI capabilities with enterprise applications, APIs, databases, and data platforms. Ensure solutions are designed for scalability, reliability, performance, and cost optimization.

RAG & Prompt Engineering :

- Design and optimize RetrievalAugmented Generation (RAG) architectures. Work with embeddings, chunking strategies, retrieval mechanisms, vector search, and context management.

- Develop, test, and optimize prompts for different foundation models and business use cases.

- Evaluate RAG pipelines for relevance, accuracy, latency, hallucination, and response quality. Work with frameworks such as LangChain and/or LlamaIndex where appropriate.

Model Evaluation & Quality :

- Establish appropriate evaluation frameworks and metrics for AI/ML/GenAI solutions.

- Review model outputs, prompts, retrieval quality, accuracy, relevance, and reliability.

- Maintain evidence of model and prompt evaluation throughout the development lifecycle.

- Identify and mitigate hallucinations, bias, inconsistent outputs, and other modelrelated risks. Define testing strategies for AI applications before production deployment.

Technical Leadership & Governance :

- Review solution designs, technical approaches, code, RAG flows, prompts, and AI architecture.

- Ensure adherence to enterprise security, architecture, coding, and engineering standards.

- Implement responsible AI principles, guardrails, access controls, and data protection mechanisms.

- Monitor AI workloads for performance, reliability, security, and cost. Establish observability and monitoring practices for AI/ML applications.

MLOps & Productionization :

- Contribute to MLOps practices covering model deployment, monitoring, versioning, testing, and lifecycle management. Support CI/CD implementation for AI/ML applications.

- Define processes for model performance monitoring and continuous improvement. Work with engineering teams to ensure smooth transition of AI solutions from development to production.

Stakeholder & Delivery Management :

- Collaborate with business stakeholders to understand objectives, pain points, and expected outcomes.

- Coordinate with architects, data engineers, application teams, DevOps, security teams, and product owners.

- Provide regular updates on delivery progress, risks, dependencies, and technical challenges.

- Participate in technical discussions, solution reviews, sprint planning, and architecture reviews.

- Ensure delivery timelines and quality standards are consistently met.

Team Mentoring :

- Coach and mentor AI/ML engineers and developers. Conduct technical knowledgesharing sessions on AWS AI, GenAI, RAG, prompt engineering, and best practices.

- Establish reusable development patterns, standards, and accelerators.

- Review team deliverables and provide technical guidance on complex AI challenges.

Required Technical Skills :

- Primary Skills : Amazon Bedrock Amazon SageMaker Generative AI / Foundation Models RetrievalAugmented Generation (RAG) Prompt Engineering Python Model Evaluation

- Secondary Skills : Vector Databases LangChain / LlamaIndex MLOps Responsible AI and Guardrails API Integration AWS AI/ML Services

Technical Competencies :

- Strong understanding of Amazon Bedrock, SageMaker, and AWS AI service integration.

- Handson knowledge of RAG architectures, embeddings, vector databases, and foundation models.

- Strong Python programming and API development/integration experience.

- Understanding of model evaluation methodologies and AI application testing.

- Knowledge of MLOps fundamentals and production AI lifecycle management.

- Understanding of responsible AI, security, privacy, guardrails, and governance.

- Ability to assess AI solution performance, scalability, reliability, and cost.

- Experience working with cloudbased AI/ML architectures.

Preferred Certifications :

AWS Certified AI Practitioner AWS Certified Machine Learning Engineer Associate AWS Certified Solutions Architect Associate

Qualification & Experience :

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

- 6 - 10 years of relevant professional experience in AI/ML, cloud engineering, data science, or related technology domains.

- Demonstrated handson experience delivering AI/ML/GenAI solutions.

- Experience leading technical teams or AI implementation squads is preferred. Strong ability to work across technical and business stakeholders.

Ideal Candidate Profile :

The ideal candidate is a handson AI/GenAI technical leader who can combine strong AWS expertise with practical AI delivery experience. The candidate should be capable of taking a business problem, identifying the right AI approach, designing the solution, guiding engineering teams, evaluating the resulting model/application, and ensuring successful production delivery.