Solutions Manager

HCL Technologies Limited; Washington (District of Columbia); Híbrido; USD 150 000 - 210 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Medical, dental, vision

401(k) plan

Paid time off

Paid holidays

Employee assistance program

Resumo da oferta

HCLTech is seeking an Agentic Forward Deployed Engineer in Washington, DC to embed with clients and transform complex business problems into production-grade autonomous agents. You will own end-to-end delivery of agentic solutions, lead a small team of 3 engineers, and ensure responsible AI, security, and governance are baked in from day one.

The role emphasizes Python, ADKs, multi-agent orchestration, and rapid prototyping to deliver measurable ROI.

Qualificações

• Strong Python coding, typed and tested, with solid software engineering principles.
• Hands-on agent building with at least one ADK.
• Experience with prompt and context engineering, RAG, and multi-agent orchestration.
• Eval-driven development and AI governance awareness.
• Standards-based integration, APIs, secure auth, CI/CD.

Responsabilidades

• Conceptualize business problems as agentic solutions and prototype quickly.
• Design and ship single- and multi-agent systems in Python using ADKs.
• Improve delivery and operational efficiency via metrics.
• Engineer agent core features: prompts, memory, tool calls, MCP integration.
• Ensure secure integration into client ecosystems.
• Lead and mentor a small team of agent engineers (3).

Conhecimentos

Python engineering

Agent development

Prompt engineering

DevSecOps

Client-facing maturity

Mentoring

Ferramentas

Google ADK

LangGraph

CrewAI

OpenAI Agents SDK

AWS Bedrock AgentCore

Semantic Kernel

Descrição da oferta de emprego

District of Columbia, District of Columbia

Job Summary

As 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.

Key Responsibilities

Frameworks: 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 same.

Models: Multi-LLM via the kit (e.g. Claude on Bedrock, Gemini, Azure OpenAI), selected per use case for quality, latency and cost.

Interfaces: Tools and Model Context Protocol (MCP) for integration; standards-based APIs and secure auth for client systems.

• Conceptualize fast: embed with stakeholders, frame a business process as an agentic solution, and stand up a working agent prototype in days, not weeks.
• Build Business Transformation Agents: design and ship single-agent and multi-agent systems in Python using ADKs that automate and transform real client workflows, with measurable ROI.
• Own efficiency as the scorecard: drive delivery efficiency and operational efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve.
• Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG / context-graph retrieval, memory, tool / function calling, MCP integration and multi-agent orchestration.
• Integrate to standards: connect agents into client ecosystems through proven integration patterns, standards-based APIs and secure authentication.
• Make reusability and predictability the default: build reusable agent components, skills, tool libraries and templates; add guardrails so agent behaviour is predictable, safe and repeatable.
• Prototype and iterate quickly: use the kit's scaffolding to prototype, then harden to production-grade, well-tested Python.
• Run eval-driven development: build evaluation harnesses and test suites that measure agent correctness, safety and regression before anything ships.
• Own AgentOps / DevSecOps: CI/CD for agents, versioning, observability and telemetry, shift-left security, and Responsible AI governance baked in from day one.
• Run a continuous, adaptable feedback loop: feed production telemetry, evals and client feedback back into prompts, context and agent design.
• Stay ahead of the curve: adopt evolving agent frameworks and patterns quickly, and bring field learnings back to the practice.
• Lead and mentor: set technical direction for a lean team of 3 agent engineers, raise the engineering bar, and grow the pod's agentic capability.

Skill Requirements

• Strong Python engineering ; idiomatic, typed, tested and packaged code; on a foundation of solid software engineering principles (design, version control, architecture).
• Hands-on agent building with at least one agent development kit (Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework / Semantic Kernel).
• Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG / context graphs, tool / function calling, MCP, and multi-agent orchestration.
• Eval-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability.
• Standards-based integration and DevSecOps: APIs, secure auth, CI/CD, observability and AgentOps.
• Ability to conceptualize a business problem as an agent quickly, and operate effectively in ambiguous, customer-embedded settings.
• Client-facing maturity: translates fluidly between technical and non-technical stakeholders, and owns outcomes.

Experience mentoring or leading small engineering teams.

Other Requirements

• Fluency across multiple ADKs and the judgment to pick the right one per engagement.
• Deploying agents to managed runtimes at enterprise scale (e.g. Vertex AI Agent Engine, Bedrock AgentCore) with governance and cost control.
• Domain depth in a transformation area - finance operations, supply chain, HR, claims or compliance.
• Experience with an enterprise agent platform, including Responsible AI and governance at scale.
• A track record of turning agents into reusable accelerators or IP adopted beyond a single engagement.

At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first.

HCLTech is a global technology company, home to more than 223,000 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. Consolidated revenues as of 12 months ending June 2026totaled $14.8billion.

Compensation and Benefits

A candidate’s pay within the range will depend on their skills, experience, education, and other factors permitted by law. This role may also be eligible for performance-based bonuses subject to company policies. In addition, this role is eligible for the following benefits subject to company policies: medical, dental, vision, pharmacy, life, accidental death & dismemberment, and disability insurance; employee assistance program; 401(k) retirement plan; 10 days of paid time off per year (some positions are eligible for need-based leave with no designated number of leave days per year); and 10 paid holidays per year.