JOB TITLE :: Senior Software Engineer - AI Platform EngineerJOB TYPE :: Full-timeJOB LOCATION :: IndiaAbout the RoleWe’re embedding AI across the entire software development lifecycle to reduce the time it takes to understand, triage, and resolve problems - from planning and code, through CI/CD, to production systems.The AI Foundation team builds the platform infrastructure that makes AI-assisted software delivery possible inside CloudBees (and our CloudBees Unify product). Our systems ingest signals from pipelines, pull requests, source-code, tests, and incidents - and need to process them reliably, at scale, in production.This is a platform engineering role. Your primary identity here is as someone who writes production-quality Go, operates confidently in Kubernetes-native environments, and takes observability seriously as a design constraint, not an afterthought.You’ll work in an environment where AI tooling is part of how the team operates - from development through to the systems we ship. That means being an effective conductor: knowing how to direct AI tooling, decompose problems for it, and apply the same critical judgement to its output that you would to any other contributor’s work.What You’ll DoPlatform EngineeringDesign, build, and operate Go services running on Kubernetes that form the backbone of our agent infrastructureOwn reliability for production systems - define SLOs, write runbooks, and be accountable when things go wrongInstrument everything: structured logging, distributed tracing (OpenTelemetry), and metrics that surface both system health and the behaviour of the AI workflows running on topContribute to Kubernetes-native patterns: operators, CRDs, workload isolation, and resource management for dynamic agent workloadsBuild and maintain CI/CD pipelines that support rapid, safe iteration - using AI tooling as a natural part of that workflowEngineering in an AI-Native EnvironmentUse AI development tooling effectively - generating, reviewing, and steering code with the same rigour you’d apply to any PRDecompose and delegate work to AI agents where appropriate, and know when not to -  catching drift, validating output, and maintaining ownership of outcomesReview AI-generated code with an understanding of its specific failure modes: plausible-looking but subtly wrong logic, missing edge cases, and over-confident implementationsContribute to the team’s shared judgement on when new models, frameworks, or tooling change the architecture assumptions we’re building onTechnical CollaborationPartner with the broader AI Foundation team to establish platform patterns that other teams adoptParticipate in architecture reviews and contribute to build vs buy decisionsMentor more junior engineers and raise the team’s bar for production readiness and operational disciplineWorking Across DisciplinesYou'll work closely with ML engineers and agentic systems engineers on the team, providing the platform foundation - reliability, observability, deployment infrastructure - that lets their work reach production safely. This is a genuine collaboration: you'll bring rigour to how their systems are built and operated, and they'll bring you along on the AI domain specifics - model behaviour, context design, evaluation approaches - as the work demands. You won't be expected to arrive knowing that space deeply; you will be expected to engage with it seriously.What We’re Looking ForRequired5+ years of professional software engineering experience, with meaningful time operating at senior levelStrong Go - idiomatic, well-tested, production-shipped (if you meet all the other requirements, but have a different language - please apply)Solid Kubernetes operations experience: you’ve debugged real production incidents, understand the scheduler, and know when a CRD is the wrong answerHands-on observability experience - OpenTelemetry, Prometheus, distributed tracing - and a genuine conviction that unobservable systems are untrustworthyCloud-native background on AWS or GCP: IAM, managed Kubernetes, infrastructure-as-codeComfortable working in an AI-augmented development environment - directing tools, critically evaluating their output, and maintaining engineering rigour throughoutDemonstrated ability to own production systems end-to-end: design, ship, monitor, iteratePreferredFamiliarity with Tekton, Jenkins, Argo Workflows, or similar pipeline infrastructureExposure to how LLM-based systems are structured - enough to reason about them as infrastructure dependencies, not black boxesExperience contributing to platform or shared infrastructure used by multiple teamsWhat Success Looks LikeFirst 3 MonthsShipping production code to the AI Foundation platform with minimal ramp-upOwning at least one service end-to-end: SLOs, alerts, and runbooks includedIdentifying and addressing a meaningful observability gap in an existing workflow3–6 MonthsDelivering a materially improved platform capability that the team relies onContributing patterns or tooling that other engineers adoptBeing the person teammates reach for on K8s and observability questions6–12 MonthsLeading a significant platform initiative from design through to production reliabilityInfluencing how the team approaches build vs buy vs integrate decisionsRaising the bar on how the team uses AI tooling in its own engineering practiceWhy This RolePlatform engineering here isn’t maintenance work. The systems are genuinely hard - dynamic workloads, heterogeneous signal sources, reliability requirements that don’t bend because the underlying models are probabilistic.You’ll also be engineering in the way most teams will be working in a few years: AI tooling as a normal part of the workflow, with engineers who know how to get the most out of it without losing ownership of what ships.