AI Platform Adoption Engineer

HCLSoftware; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

HCLSoftware Bengaluru seeks an experienced professional to close the gap between AI platform capability and real-world usage within a highly technical, developer-focused environment. You will embed with engineering to identify friction, adoption blockers, and drive concrete fixes as the primary interface between platform operations and the engineering org.

Responsibilities include maintaining an integration catalog, monitoring usage analytics, evaluating new AI capabilities, and ensuring

Qualifications

• 4+ years of hands-on software engineering or platform engineering.
• Direct 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.

Responsibilities

• Embed with engineering and technical teams to assess workflows and identify friction points.
• Diagnose and resolve platform adoption blockers at the integration level (IDE, API, SSO, network issues).
• Own the platform integration catalog and keep it current as tools evolve.
• Monitor usage analytics by team and platform; investigate low-adoption patterns and drive interventions.
• Evaluate new AI platform capabilities and integrations and recommend adoption or deferral with technical reasoning.
• Manage the internal AI platform feedback loop and route issues to vendors with specificity.
• Support governance enforcement around configurations, data handling, and access controls.
• Collaborate with support to distinguish systemic issues from user errors and escalate as needed.

Skills

Hands-on software engineering

Platform engineering

Developer tooling

Technical conversations with senior IC

Usage analytics & dashboards

Tools

IDE integrations

CLI tools

API authentication

Proxy and network configuration

CI/CD pipelines

Version control

Job description

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; escal​ate 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.

Preferred Qualifications

• Working knowledge across multiple enterprise AI platforms at a technical integration depth — Anthropic Claude (Code and/or Cowork), Google Gemini for Workspace, OpenAI ChatGPT Enterprise — sufficient to compare integration behaviors, troubleshoot platform-specific issues, and advise on fit.
• Experience in a developer platform, internal tooling, or developer experience (DX) function within a software product company.
• Familiarity with agentic AI deployment patterns (tool use, MCP servers, multi-step workflows) at an operational level — understanding what can go wrong and how to govern it, not how to build it.
• Background in enterprise developer tooling ecosystems: IDE plugin administration, GitHub/GitLab integration, developer portal management.