AI / ML Engineer

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : AI Agents & Workflow Integration Good to have skills : NA Minimum 5 year(s) of experience is required Educational Qualification : 15 years full time education Role Overview : Forward Deployed AI Engineers form the execution spine of our Reinvention Deployment Engineering pods. We are building the largest FDE capability in the services industry. The engineers who join at this stage will define what the role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry. Roles & Responsibilities: Embed directly with client engineering and business teams to deploy, scale, and operationalize AI platforms — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — inside enterprise environments Own production outcomes end-to-end: time-to-value, reliability, adoption velocity, and scalability, with business metrics attached — not just delivery milestones Move from ambiguous business problem to working production system through rapid experimentation: days to prototype, weeks to production-ready Design and govern AI architectures across the full enterprise stack: identity, data, security, governance, platform layer, and workflow integration Translate technical architecture into business impact for client CTO, CFO, and CISO shape use case roadmaps, ROI backlogs, and AI adoption strategy Build reusable patterns, playbooks, and accelerators that the client owns after you leave — enabling the client team to run it without you Lead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client engineering and leadership teams Codify patterns and delivery learnings that scale across engagements and contribute to the growth of the FDE practice. Professional & Technical Skills: Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless). Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments. Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines. Experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging. Demonstrated end-to-end delivery ownership in a client-embedded environment, internal projects, vendor labs, or team-only deployments do not qualify Proven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and act on