8+ years of experience in design and develop AI solution including Gen AI ensuring scalability, security, governance and cost optimization
Expert level knowledge in MLOps
Expertise in cloud-based AI platforms such as Amazon SageMaker, Amazon Bedrock, Databricks, and Kubernetes.
Experience with AWS cloud infrastructure, networking, and IAM services.
Proven ability to define AI capability models and translate them into platform solutions, enterprise roadmaps, and scalable architectures.
Experience with Gen AI & LLMs, including frameworks such as RAG (retrieval-augmented generation), Agents and Prompt Engineering
Hands-on experience and understanding with Gen AI and ML lifecycle and tools such as ML model Development and training, MLFlow, Feature Stores, Chaining Framework (LangChain, Llama Index, etc) and Vector DBs are good to have.
Strong understanding of AI security and responsible AI practices, including bias detection, explainability and model fairness.
Familiarity with AI governance practices.
Experience in API and microservices design.
Good understanding of data lake, data mesh, and data fabric architectures.
Responsibilities:
Design and architect ML and Gen AI platform solutions, supporting the full lifecycle.
Architect secure and scalable Gen AI platform solutions ensuring platform reusability and ethical AI practices.
Establish standards, patterns, and frameworks for developing, deploying and monitoring production-grade ML models and Gen AI applications
Design and drive the adoption of MLOps and LLMOps best practices optimizing for efficiency, scalability, and compliance.
Ensure compliance with IT security policies and drive security reviews in collaboration with security team.
Present architectural designs to the review board (ARBs) for evaluation and approval
Develop Gen AI and ML platform capability model, identifying gaps in tools, skills, and infrastructure across the data and AI platform.
Working closely with Product Owner to define a strategic roadmap for scaling Gen AI Platform capabilities and implementation, prioritizing business-aligned use cases
Collaborate with AI governance team to define governance frameworks for model lifecycle management, security (RBAC, encryption), and compliance.
Engage with business stakeholders and other AI professionals to identify and assess AI use cases, ensuring feasibility and business impact.
Translate AI vision into technical solutions, ensuring alignment with enterprise architecture.
Work closely with cross-functional teams to ensure successful implementation of scalable and secure AI solutions.
Design APIs and microservices to expose Gen AI and ML capabilities as reusable components within the data platform.
Partner with engineering teams to automate CI/CD pipelines for model and artifacts deployment and monitoring (MLFlow, Vector DBs, etc)
Research and assess emerging trends in Gen AI, LLMs, and ML (e.g., synthetic data, federated learning, AI security).
Mentor teams on best practices for Gen AI and ML development, platform optimization, and ethical AI principles.
Publish reference architectures and best practices to enhance AI adoption and platform capabilities.
Lead evaluation and selection of Gen AI and ML technologies and frameworks (e.g., Langchain, Vector DBs, Model serving frameworks)
Drive proof-of-concept initiatives to assess emerging AI tools and their alignment with platform needs.