Artificial Intelligence Engineer

HCLTech; Jigani; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

HCLTech is seeking an AI Platform Adoption Engineer to bridge the gap between platform capabilities and real-world usage in a development-driven environment in Karnataka. You will embed with engineering and technical teams to identify adoption friction, diagnose blockers, and drive technical interventions that enable productive use of AI platforms across multiple projects and pipelines.

The role emphasizes hands-on integration work, collaboration with platform ops, and governance alignment to

Qualifications

• 4+ years of hands-on software or platform engineering experience.
• Experience using enterprise AI platforms in a professional software development context.
• Strong diagnostic skills across the developer toolchain: IDE integrations, CLI tools, API authentication, proxy and network configuration, CI/CD pipelines, and version control systems.
• Priven ability to engage with tenured technical professionals as a peer; able to assess workflows independently and make credible recommendations without being directed.
• Experience with enterprise SSO — SAML or OIDC — as it applies to third-party developer tool integration.

Responsibilities

• Embed with engineering teams to assess tool fit and adoption barriers.
• Diagnose and resolve platform integration blockers like IDE plugins and API auth.
• Maintain platform integration catalog across systems and environments.
• Monitor usage analytics and drive targeted adoption interventions.
• Evaluate new AI platform capabilities for fit with internal workflows.
• Collaborate on governance, data handling, and access controls.

Skills

Python

JavaScript/TypeScript

Go

Java

Bash/PowerShell

Tools

Anthropic Claude

GitHub Copilot

Cursor

Google Gemini for Workspace

OpenAI ChatGPT Enterprise

Job description

AI Platform Adoption Engineer

About the Role

This role exists to close the gap between AI platform capability and real-world usage in a technical, developer-heavy organization. You will embed directly with engineering and technical teams to identify where AI platforms are creating friction, where adoption is stalling, and why and then fix it. This is not a training or documentation role. Your users are experienced software developers and senior technical professionals who need a peer-level technical resource who understands both their workflows and the AI platform landscape deeply enough to drive meaningful change. You will work closely with the Team Lead and operate as the primary interface between the platform operations team and the engineering organization.

Scope boundary

This role focuses on platform adoption, workflow integration, and friction removal not AI skill development. Prompt engineering methodologies, RAG pipeline design, vector search implementation, and AI application architecture are owned by the Engineering team. This role does not duplicate or overlap with that charter.

Responsibilities

• Embed with engineering and technical teams to conduct structured workflow assessments: identify where AI tools fit, where they are underused, and what operational or integration barriers are preventing adoption.
• Diagnose and resolve adoption blockers at the platform integration level — IDE plugin configuration, API authentication, network/proxy issues, enterprise SSO integration, and tool compatibility — working alongside developers, not around them.
• Own the platform integration catalog: document which AI tools work with which internal systems, pipelines, and developer environments, and keep it current as platforms evolve.
• Monitor usage analytics by team and platform; identify low-adoption patterns, investigate root causes, and drive targeted interventions in partnership with team leads.
• Evaluate new AI platform capabilities and integrations as they release; assess fit against internal workflows and recommend adoption or deferral with technical justification.
• Manage the internal AI platform feedback loop: collect structured signal from developers on capability gaps and platform pain points, synthesize findings, and route them to vendors with technical specificity.
• Partner with the Team Lead on platform governance enforcement: ensure teams are operating within approved configurations, data handling policies, and access controls.
• Collaborate with the Support Engineer to identify systemic platform issues vs. individual user errors; escalated and coordinate resolution with vendors when warranted.

Required Qualifications

• 4+ years of hands-on software engineering, platform engineering, or developer tooling experience; must be able to read and write code and hold a credible technical conversation with senior engineers.
• Direct experience using enterprise AI platforms in a professional software development context — not just familiarity, but evidence of integrating AI tools into real engineering workflows.
• Strong diagnostic skills across the developer toolchain: IDE integrations, CLI tools, API authentication, proxy and network configuration, CI/CD pipelines, and version control systems.
• Proven ability to engage with tenured technical professionals as a peer; able to assess workflows independently and make credible recommendations without being directed.
• Comfort with usage analytics and data: able to pull platform telemetry, build adoption dashboards, and translate metrics into operational decisions.

Mandatory Skills

• Coding: Working proficiency in at least one of Python, JavaScript/TypeScript, Go, Java, or Bash/PowerShell — sufficient to read unfamiliar code, write automation scripts, and reproduce a developer's problem locally.
• Enterprise AI tooling: Hands-on work with at least one of Anthropic Claude (Code or Cowork), GitHub Copilot, Cursor, Google Gemini for Workspace, or OpenAI ChatGPT Enterprise in a deployment, administration, or integration capacity.
• Toolchain breadth: Demonstrated troubleshooting in at least three of: IDE extension deployment (VS Code, JetBrains); CLI tooling; REST APIs with OAuth, API keys, or bearer tokens; corporate proxies, TLS inspection, or network egress controls; CI/CD (GitHub Actions, Jenkins, GitLab CI, Azure DevOps); Git-based source control (GitHub, GitLab, Bitbucket).
• Identity: Practical understanding of enterprise SSO — SAML or OIDC — as it applies to third-party developer tool integration.