Agentic Forward Deployed Engineer

HCLTech is a global technology company, home to more than 220,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. About the rol eAs an Agentic Forward Deployed Engineer, you operate at the front line of delivery - embedded with the client, turning ambiguous business problems into production agents, fast. Your deliverable is Business Transformation Agents: autonomous and multi-agent systems that automate and reimagine real business processes such as invoice disputes, procurement approvals, onboarding, claims and compliance workflows. You own each agent end to end -conceptualize, build, integrate, evaluate, deploy, and sustain - and you lead a small team to do the same. You build exclusively in Python using agent development kits, and you bring Agentic AI capabilities to life inside the client's world, with Responsible AI, evaluation and security as non-negotiables . Technology manda teLanguage : Python preferab leFrameworks : Agent Development Kits (ADKs) ; e.g. Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore, Microsoft Agent Framework / Semantic Kernel. Framework choice follows the engagement; the discipline is the sam e.Models : Multi-LLM via the kit (e.g. Claude on Bedrock, Gemini, Azure OpenAI), selected per use case for quality, latency and cos t.Interfaces : Tools and Model Context Protocol (MCP) for integration; standards-based APIs and secure auth for client system s. What you'll * doConceptualize fas t: embed with stakeholders, frame a business process as an agentic solution, and stand up a working agent prototype in days, not wee * ks.Build Business Transformation Agent s: design and ship single-agent and multi-agent systems in Python using ADKs that automate and transform real client workflows, with measurable R * OI.Own efficiency as the scorecar d: drive delivery efficiency and operational efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-ser * ve.Engineer the agent cor e: apply prompt engineering, context engineering, prompt caching, RAG / context-graph retrieval, memory, tool / function calling, MCP integration and multi-agent orchestrati * on.Integrate to standard s: connect agents into client ecosystems through proven integration patterns, standards-based APIs and secure authenticati * on.Make reusability and predictability the defaul t: build reusable agent components, skills, tool libraries and templates; add guardrails so agent behaviour is predictable, safe and repeatab * le.Prototype and iterate quickl y: use the kit's scaffolding to prototype, then harden to production-grade, well-tested Pyth * on.Run eval-driven developmen t: build evaluation harnesses and test suites that measure agent correctness, safety and regression before anything shi * ps.Own AgentOps / DevSecOp s: CI/CD for agents, versioning, observability and telemetry, shift-left security, and Responsible AI governance baked in from day o * ne.Run a continuous, adaptable feedback loo p: feed production telemetry, evals and client feedback back into prompts, context and agent desi * gn.Stay ahead of the curv e: adopt evolving agent frameworks and patterns quickly, and bring field learnings back to the practi * ce.Lead and mento r: set technical direction for a lean team of 3 agent engineers, raise the engineering bar, and grow the pod's agentic capabili ty.What you'll bring (must-ha * ve)Strong Python engineering ; idiomatic, typed, tested and packaged code; on a foundation of solid software engineering principles (design, version control, architectur * e).Hands-on agent buildi ng with at least one agent development kit (Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework / Semantic Kerne * l).Solid command of agent engineerin g: prompt engineering, context engineering, prompt caching, RAG / context graphs, tool / function calling, MCP, and multi-agent orchestrati * on.Eval-driven developmen t: designing evaluation harnesses and measuring agent quality, safety and reliabili * ty.Standards-based integration and DevSecOp s: APIs, secure auth, CI/CD, observability and AgentO * ps.Ability to conceptualize a business problem as an agent quickl y, and operate effectively in ambiguous, customer-embedded settin * gs.Client-facing maturit y: translates fluidly between technical and non-technical stakeholders, and owns outcom * es.Experience mentoring or leadi ng small engineering tea ms.What great looks like (strongly preferr * ed)Fluency across multiple AD Ks and the judgment to pick the right one per engageme * nt.Deploying agents to managed runtim es at enterprise scale (e.g. Vertex AI Agent Engine, Bedrock AgentCore) with governance and cost contr * ol.Domain dep th in a transformation area - finance operations, supply chain, HR, claims or complian * ce.Experience with an enterprise agent platform, including Responsible AI and governance at sca * le.A track record of turning agents into reusable accelerato rs or IP adopted beyond a single engageme nt.