Senior Software Engineer AI Platform Engineer

JOB TITLE :: Senior Software Engineer - AI Platform Engineer

JOB TYPE :: Full-time

JOB LOCATION :: India Chennai (Hybrid)

Were 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.

Youll 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 contributors work.

• Design build and operate Go services running on Kubernetes that form the backbone of our agent infrastructure
• Own reliability for production systems - define SLOs write runbooks and be accountable when things go wrong
• Instrument everything: structured logging distributed tracing (OpenTelemetry) and metrics that surface both system health and the behaviour of the AI workflows running on top
• Contribute to Kubernetes-native patterns: operators CRDs workload isolation and resource management for dynamic agent workloads
• Build and maintain CI/CD pipelines that support rapid safe iteration - using AI tooling as a natural part of that workflow

• Use AI development tooling effectively - generating reviewing and steering code with the same rigour youd apply to any PR
• Decompose and delegate work to AI agents where appropriate and know when not to - catching drift validating output and maintaining ownership of outcomes
• Review AI-generated code with an understanding of its specific failure modes: plausible-looking but subtly wrong logic missing edge cases and over-confident implementations
• Contribute to the teams shared judgement on when new models frameworks or tooling change the architecture assumptions were building on

• Partner with the broader AI Foundation team to establish platform patterns that other teams adopt
• Participate in architecture reviews and contribute to build vs buy decisions
• Mentor more junior engineers and raise the teams bar for production readiness and operational discipline

• Youll 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: youll bring rigour to how their systems are built and operated and theyll bring you along on the AI domain specifics - model behaviour context design evaluation approaches - as the work demands.
• You wont be expected to arrive knowing that space deeply; you will be expected to engage with it seriously.

• 5 years of professional software engineering experience with meaningful time operating at senior level
• Strong Go - idiomatic well-tested production-shipped (if you meet all the other requirements but have a different language - please apply)
• Solid Kubernetes operations experience: youve debugged real production incidents understand the scheduler and know when a CRD is the wrong answer
• Hands-on observability experience - OpenTelemetry Prometheus distributed tracing - and a genuine conviction that unobservable systems are untrustworthy
• Cloud-native background on AWS or GCP: IAM managed Kubernetes infrastructure-as-code
• Comfortable working in an AI-augmented development environment - directing tools critically evaluating their output and maintaining engineering rigour throughout
• Demonstrated ability to own production systems end-to-end: design ship monitor iterate

• Familiarity with Tekton Jenkins Argo Workflows or similar pipeline infrastructure
• Exposure to how LLM-based systems are structured - enough to reason about them as infrastructure dependencies not black boxes
• Experience contributing to platform or shared infrastructure used by multiple teams

• Shipping production code to the AI Foundation platform with minimal ramp-up
• Owning at least one service end-to-end: SLOs alerts and runbooks included
• Identifying and addressing a meaningful observability gap in an existing workflow

• Delivering a materially improved platform capability that the team relies on
• Contributing patterns or tooling that other engineers adopt
• Being the person teammates reach for on K8s and observability questions

• Leading a significant platform initiative from design through to production reliability
• Influencing how the team approaches build vs buy vs integrate decisions
• Raising the bar on how the team uses AI tooling in its own engineering practice

Platform engineering here isnt maintenance work. The systems are genuinely hard - dynamic workloads heterogeneous signal sources reliability requirements that dont bend because the underlying models are probabilistic.

Youll 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.