Forward Deployed Engineer

Capgemini; Bengaluru; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Capgemini is seeking a Forward Deployed Engineer in Bengaluru to partner with business, product, and engineering teams to reimagine processes using AI and agentic systems. You will own AI-led solutions from discovery through production deployment, translating complex business problems into scalable technical architectures.

The role requires strong systems thinking, hands-on AI/ML experience, and the ability to work with non-technical stakeholders in a fast-moving environment.

Qualifications

• Hands-on experience building and deploying AI/ML or LLM-powered applications.
• Experience translating ambiguous business problems into working technical solutions.
• Comfort working directly with non-technical stakeholders in fast-moving, high-ambiguity environments.
• Strong systems thinking and ability to design end-to-end workflows.

Responsibilities

• Discovery & Problem Framing: translate business needs into clear problem statements and requirements.
• Solution Design: architect end-to-end AI-led solutions with scalable, secure designs.
• Build & Iterate: prototype, test, and iterate AI-enabled workflows and agentic components.
• Deployment & Adoption: deploy to production with monitoring and governance; drive adoption.

Skills

AI/ML applications

LLM-powered applications

Stakeholder collaboration

Systems thinking

Job description

Forward Deployed Engineer - AI & Agentic Solutions

Role Overview

The Forward Deployed Engineer (FDE) will partner closely with business, product, and engineering teams to reimagine end-to-end processes at Capgemini using AI and agentic systems. This role sits at the intersection of business design, strategy, product design, and hands-on engineering, owning AI led solutions from problem discovery through production deployment.

FDEs will work directly with stakeholders to identify high-impact opportunities, translate ambiguous business problems into technical architectures, and rapidly build, test, and deploy AI-powered solutions that materially transform how work gets done.

Key Responsibilities

Discovery & Problem Framing

• Partner with business and operational teams to deeply understand existing workflows, pain points, constraints, and business objectives, helping shape future-state operating models enabled by AI and agentic solutions
• Identify opportunities where AI, LLMs, and agentic systems can drive step-change improvements in efficiency, quality, or user experience
• Translate loosely defined business challenges into clear problem statements, success metrics, and technical requirements
• Business Design & Value Realization
• Partner with stakeholders to define target-state business processes and operating models enabled by AI and agentic systems.
• Facilitate workshops to align business goals, user needs, process redesign, and AI capabilities.
• Develop business cases, value hypotheses, and adoption strategies for AI-led transformation initiatives.
• Ensure proposed solutions deliver measurable business outcomes beyond task-level automation.

Solution Design

• Design end-to-end AI-led solutions and agentic system architectures, including business workflows, data flows, model selection, orchestration patterns, and human-in-the-loop controls.
• Collaborate with product, risk, and platform teams to ensure solutions are scalable, secure, explainable, and compliant
• Rapidly prototype concepts to validate feasibility and value before full build-out

Build & Iterate

• Implement AI solutions using modern stacks (e.g., Python, APIs, LLM frameworks, vector databases, workflow engines)
• Build and orchestrate agentic workflows that can reason, plan, and take actions across systems
• Iterate quickly based on stakeholder feedback, usage data, and performance metrics

Deployment & Adoption

• Deploy solutions into production environments with appropriate monitoring, logging, and guardrails
• Partner with platform and operations teams to ensure reliability, scalability, and maintainability
• Support organizational change management, user adoption, and value realization by working closely with business owners, end users, and delivery teams

Required Qualifications

• Hands-on experience building and deploying AI/ML or LLM-powered applications
• Experience translating ambiguous business problems into working technical solutions
• Comfort working directly with non-technical stakeholders in fast-moving, high-ambiguity environments
• Strong systems thinking and ability to design end-to-end workflows

Preferred Qualifications

• Experience with agentic frameworks, orchestration tools, or multi-agent systems
• Familiarity with enterprise data platforms, APIs, and workflow automation tools
• Background in process improvement, operations, or digital transformation initiatives
• Experience working in regulated or complex enterprise environments
• Ability to balance speed of delivery with robustness, security, and governance

What Success Looks Like

• Business teams can point to materially improved processes enabled by your solutions
• AI and agentic systems you deploy are used, trusted, and scaled
• Ambiguous ideas are quickly turned into production-ready solutions
• Stakeholders view you as both a technical expert and a trusted problem-solving partner

Role Differentiation

• Autonomous Platform AI Engineer: Builds and operationalizes models, agents, and AI infrastructure. Responsible for build out of Agentic AI tech stack.
• Quality Intelligence Engineer: Creates AI-powered testing assets including test scripts, automation scripts, test data management and generation.
• Reliability Intelligence Engineer: Ensures operational reliability, observability, and resilience of test and production environments including CI/CD along with DevOps AI Agents.
• Solution Architect: Defines end-to-end architecture, standards, and governance including definition of AI Guard Rails, hands on understanding of AI Tech Stack and change processes. Driver of AI-First Agentic SDLC methodology
• AI Product Owner: Defines business objectives, product vision, adoption strategy, and prioritization. Prioritization and Governance of product item backlog especially the AI assets both technical and functional.
• AI-Augmented Software Engineer: Builds enterprise software solutions using AI-assisted engineering practices
• Forward Deployed Engineer: Owns discovery, business design, AI led solution, rapid prototyping, and ensuring delivery
• Knowledge & Enablement Engineer: Creates documentation, knowledge assets, training programs, and adoption content using AI-assisted techniques