Track Lead (Support & Operations) at HCLTech · Gautam…

Job Summary

FORWARD DEPLOYMENT ENGINEER - OpenAI

Classification	Internal
Location	Noida, Hyderabad, Chennai, Bangalore, Pune
Job Type	Full-Time
Experience	4–7 years, with at least 2 years of hands-on experience in Generative AI / Agentic AI solutions and production implementation.

Role Overview

We are looking for a Forward Deployment Engineer (FDE) to design, build, integrate, deploy, troubleshoot, and operationalize enterprise Generative AI and Agentic AI solutions using OpenAI. The FDE will work directly with customer technical and business stakeholders to understand their environment and requirements, translate them into deployable AI solutions, integrate agents with enterprise applications and data sources, configure security and governance controls, and drive solutions through development, validation, production deployment, go-live, and hypercare.

The role requires strong software engineering and API development skills combined with hands-on experience in LLMs, agentic AI, enterprise integrations, AI security, evaluation, and production operations.

Key Responsibilities

Customer Engagement and Solution Delivery Work directly with customers to understand business, technical, integration, security, data, and operational requirements. Conduct technical discovery sessions, workshops, demonstrations, proof-of-concepts, pilot implementations, and deployment planning. Translate customer requirements into practical Generative AI and Agentic AI solutions. Work with customer architects, application teams, security teams, data teams, and operations teams to drive successful deployment. Own assigned customer technical workstreams from implementation through production and hypercare. Agent and AI Solution Engineering Design, develop, configure, and deploy enterprise AI agents using OpenAI. Build and customize agent instructions, prompts, Skills, tools, workflows, memory/context patterns, and agent handoffs. Implement multi-agent and tool-driven workflows appropriate to the customer use case. Develop customer-specific logic and orchestration in Python or other appropriate programming languages. Implement tool calling, structured outputs, context management, and reliable error handling for production AI applications. Enterprise Integration Integrate AI agents with enterprise applications using REST APIs, SDKs, webhooks, databases, message queues, and MCP. Build customer-specific integrations where reusable connectors or tools are not available. Integrate with enterprise systems such as ServiceNow, Jira, SAP, Salesforce, Microsoft 365, SharePoint, databases, monitoring platforms, and proprietary customer applications. Configure authentication, authorization, OAuth, API keys, certificates, secrets, and role-based access controls. Implement appropriate approval and human-in-the-loop mechanisms for agent actions. Map customer-specific data models, business rules, fields, permissions, and workflows. Data and Knowledge Integration Integrate enterprise knowledge sources and customer data into AI applications. Implement RAG and retrieval solutions using enterprise search, vector databases, APIs, file repositories, and knowledge sources as appropriate. Configure chunking, retrieval, filtering, metadata, access control, and relevance mechanisms as required. Ensure retrieved enterprise information respects customer authorization and data-access requirements. Security, Governance and Responsible AI Implement enterprise security requirements for AI applications and agentic workflows. Configure access controls, permissions, secrets, data protection, auditability, and security policies. Support customer requirements for data retention, privacy, compliance, and model usage. Implement guardrails and approval controls for tool execution and high-impact actions. Work with customer security and compliance teams to complete technical assessments and production approvals. Evaluation and Observability Implement evaluation frameworks for prompts, agents, tools, RAG, and end-to-end workflows. Create representative test datasets, golden datasets, regression tests, and production validation scenarios. Monitor agent quality, hallucination, tool accuracy, latency, failures, token consumption, and cost. Implement application-level logging, tracing, monitoring, and audit trails. Investigate agent traces and production failures and identify corrective actions. Support continuous evaluation and regression testing when models, prompts, tools, or integrations change. Deployment and Production Deploy customer AI applications and supporting services into the customer approved environment. Integrate AI services with customer applications, middleware, API gateways, containers, serverless infrastructure, or Kubernetes environments as appropriate. Implement CI/CD pipelines, environment promotion, configuration management, version management, rollback, and release processes. Perform functional, integration, security, performance, resiliency, and user acceptance testing. Support production go-live, hypercare, incident resolution, and tr

Skill Requirements

Platform-Specific Must Have Skills

• Hands-on experience with OpenAI API/platform, including the Responses API and OpenAI Agents SDK.
• Experience with function/tool calling, structured outputs, file/search capabilities, and MCP-based integrations.
• Experience designing applications around OpenAI models, model selection, context management, prompt engineering, and production API usage.
• Experience with enterprise OpenAI deployments, governance, usage controls, security, and operational practices is highly desirable.

Must Have Skills

• Strong hands-on programming experience in Python.
• Strong understanding of LLMs, Generative AI, RAG, prompt engineering, agentic AI, tool calling, and multi-agent workflows.
• Strong experience developing and consuming REST APIs and enterprise integrations.
• Strong understanding of authentication and authorization, including OAuth, API keys, RBAC, secrets management, and access controls.
• Strong Python software engineering practices including Git, testing, debugging, packaging, asynchronous programming, and error handling.
• Experience deploying production AI applications using Docker, Kubernetes, serverless platforms, or enterprise application platforms.
• Experience with CI/CD and production software deployment.
• Experience with AI observability, evaluation, logging, tracing, and production troubleshooting.
• Experience with enterprise data sources, databases, APIs, search, and RAG.
• Understanding of Responsible AI, AI security, privacy, compliance, and enterprise governance.
• Strong communication skills and ability to work directly with enterprise customers.
• Ability to independently take a customer requirement from discovery through production.

Preferred Skills

• Experience with OpenAI enterprise capabilities, organization/workspace controls, usage governance, and administration is advantageous.
• Experience with model gateways or model routers supporting OpenAI models.
• Experience with OpenAI-specific evaluation, tracing, and operational tooling or equivalent third-party tooling.
• Experience with ChatGPT Enterprise and enterprise AI adoption is advantageous.
• Experience with ServiceNow, ITSM, CloudOps, SRE, AIOps, or enterprise automation use cases.

Other Requirements

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or related field.
• 4–7 years of overall software engineering, cloud, or AI engineering experience.
• Minimum 2 years of practical Generative AI / Agentic AI experience.
• Demonstrated experience delivering at least one AI solution into a production environment.
• Demonstrated experience working with enterprise customers or customer-facing engineering teams.

Key Attributes

• Strong customer-facing presence and ability to build technical trust.
• Strong programming and debugging capability.
• Comfortable working across AI, APIs, enterprise applications, data, security, and operations.
• Strong ownership and bias for execution.
• Able to distinguish customer-specific customization from reusable platform capabilities.
• Comfortable working in ambiguous and rapidly changing AI environments.
• Strong problem-solving and troubleshooting ability.