Customer Success Engineer

Job Summary

We are looking for a Customer Success Engineer who is fundamentally a developer - someone who finds genuine excitement in engineering new solutions, enjoys rolling up their sleeves in customer environments, and brings both technical depth and a consulting mindset to every engagement.

This is not a support or relationship management role. You will work directly in customer codebases, CI/CD pipelines, and API platforms to get things working and leave them in better shape than you found them. You will own the technical journey from onboarding through platform activation and be the customers most trusted technical advocate throughout.

You will be joining a small, high-impact team covering India and APAC. The work is hands-on, fast-moving, and consequential.

Responsibilities

• Customer Onboarding and Platform Activation Drive enterprise customers from contracts close to a fully activated developer platform. Own the technical setup: identity and access controls (SSO, SCIM, RBAC), workspace configuration, Git integration, CI/CD pipeline onboarding, and API catalog setup. Ensure customers have a governed foundation in place within 30-60 days.
• SDLC Automation and CI/CD Integration Build automated pipelines that run contract tests, governance checks, and artifact syncs on every commit. Integrate platform tooling into standard CI/CD systems - GitHub Actions, GitLab CI, Azure DevOps, Jenkins. Leave behind documented patterns the customer team can own and extend.
• Security and Identity Configure and troubleshoot enterprise authentication and identity controls - SSO (SAML, OIDC), SCIM provisioning, OAuth 2.0, API key and token management, secrets vaults, and RBAC. Communicate credential handling clearly to security and compliance stakeholders.
• AI and Agentic Platform Activation Help customers adopt AI-powered and agentic platform capabilities. Demonstrate and align how well-governed APIs and automation layers reduce token waste, improve agent accuracy, and enable safe AI tooling at scale. Connect platform features to real engineering productivity outcomes.
• Technical Consulting and Stakeholder Engagement Run structured technical discovery. Define scope, success criteria, and delivery milestones. Communicate clearly with both engineering teams and executive stakeholders. Translate implementation outcomes into measurable business value - velocity, quality, cost, and risk reduction.
• Engineering Quality Awareness Bring knowledge of engineering quality frameworks - DORA metrics, shift-left testing, QA maturity models, contract testing, and governance scoring. Use this lens to show customers where current practices create risk and where automation creates measurable improvement. Connect the platform to outcomes engineering leaders care about: deployment frequency, change failure rate, mean time to recovery, and test coverage.
• Customer Self-Sufficiency Build reusable assets, templates, and playbooks. Leave every engagement with documented ownership and the customer team capable of running and extending what you built together.
• Thought Leadership and Voice of the Customer When you encounter a friction point - a missing pattern, a recurring customer pain, a product gap - document it so the next person does not have to solve it from scratch. Build reusable knowledge: runbooks, root-cause logs, and playbooks that make the whole team faster. Bring recurring customer challenges to Product and Engineering with enough specificity to drive a real fix - so a problem one customer faces today does not become a problem every customer faces next quarter.

About You

Technical depth

• Scripting and automation in Python, Node.js, or similar - including calling APIs and building workflow automations
• Cloud and infrastructure reasoning: containers, permissions, secrets, and deployment environments
• Familiarity with engineering quality frameworks: DORA metrics, contract testing, shift-left QA, and test coverage models
• Ability to build beyond a product's UI: scripts, integrations, pipelines, and tooling that solve real customer problems

AI and agentic experience

• Practical experience using LLMs or generative AI tools in technical work - scripting, automation, documentation, or integration
• Familiarity with agentic frameworks, MCP, or similar approaches to governing AI-driven workflows
• Sound judgment about AI outputs: you validate, test, and do not ship generated work without review

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.