AI / ML Engineer

Lead the design, architecture, and delivery of enterprise-scale Automation, GenAI, and Agentic AI solutions. Drive technology transformation initiatives by defining scalable architectures, deployment strategies, data architectures, and governance frameworks while ensuring alignment with business objectives, security standards, and operational requirements.
Roles & Responsibilities
Define end-to-end solution architecture for Automation, GenAI, and Agentic AI initiatives across application, integration, data, infrastructure, and security layers.
Design scalable automation solutions leveraging RPA, APIs, workflow orchestration, AI/ML, and cloud-native technologies.
Architect and implement GenAI and Agentic AI solutions using frameworks such as Google ADK, LangGraph, LangFlow, AutoGen, and enterprise LLM platforms.
Define deployment architecture, environment strategy, CI/CD pipelines, monitoring, observability, and production support models.
Design data architecture including data ingestion, transformation, storage, indexing, retrieval, vector search, and governance frameworks.
Establish integration patterns with enterprise applications, ITSM platforms, databases, knowledge repositories, and cloud services.
Conduct architecture reviews, technology assessments, solution estimations, and design governance activities.
Ensure compliance with enterprise security, privacy, regulatory, and responsible AI standards.
Collaborate with business stakeholders to identify automation opportunities and translate business requirements into technical solutions.
Lead technical teams and provide architecture guidance, mentoring, and design oversight throughout the project lifecycle.
Drive innovation by evaluating emerging technologies, AI frameworks, and automation platforms to enhance enterprise capabilities.
Support proposal development, solutioning, client presentations, and strategic technology roadmaps.
Define non-functional requirements including scalability, reliability, performance, resilience, and disaster recovery.
Partner with operations teams to establish support models, monitoring frameworks, and continuous improvement processes.
Deliver measurable business outcomes through automation, AI adoption, and operational excellence initiatives.