AI / ML Engineer

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 Experience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level Non-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching.

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 Experience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level Non-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching. Additional Information: The candidate should have minimum 5 years of experience in AI Agents & Workflow Integration.3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless). All AI Native Engineer foundations confirmed, non-negotiable baseline Full platform deployment ownership in at least one certified platform in a live client environment Multi-system integration: connects AI platform to ERP, CRM, ITSM, and data systems in production Enterprise architecture foundations: identity, data, security, and governance layers, not just application layer Runs deployment cycles independently, from kickoff to production with client handover This position is based at our Bengaluru office. A 15 years full time education is required.