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Staff Software Engineer (Agentic Systems) – AI for Software Delivery

CloudBees -
Indi, Karnataka

Apply Now

Job details

Full-time

Qualifications

CI/CD
Operating systems
Go
Law
Git
Master's degree
AWS
Distributed systems
Continuous integration
GitHub
AI
Jenkins
TypeScript
Python
SDLC

Full job description

Job Title: Staff Software Engineer (Agentic Systems) – AI for Software Delivery
Job Type: Full-Time, Remote

About CloudBees

CloudBees helps enterprises deliver secure, compliant software at scale. We sit at the center of the software delivery lifecycle, from Jenkins-based CI to modern cloud-native DevSecOps platforms.

Our focus is simple: make software delivery faster, safer, and more reliable for large engineering organizations.

About the Role

We’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.

As a Staff Engineer, you will lead the design and evolution of agentic systems that operate across the SDLC. These systems ingest signals from multiple sources - code, pull requests, tests, pipelines, incidents, and documentation - and reason over them to provide high-confidence insights and, over time, take safe, constrained actions.

This is not just about adding AI to existing workflows. It’s about defining how intelligent systems participate in software delivery, where context is fragmented, systems are interdependent, and correctness matters.

You will work across team and product boundaries to:

Define architectures for cross-SDLC agentic workflows
Establish patterns for reasoning, context synthesis, and tool orchestration
Connect signals across traditionally siloed systems (e.g. code ? CI ? incidents ? docs)
Drive the evolution from advisory systems to controlled, auditable automation
Ensure these systems are reliable, observable, and safe in production environments

This role requires both deep technical execution and strong technical leadership, including shaping direction, influencing multiple teams, and raising the bar for how AI systems are built and operated.

What You’ll Do

Lead the design of AI-driven systems that triage, explain, and eventually remediate CI/CD failures across products
Define and evolve agent architectures, including reasoning strategies, tool orchestration, and context management
Drive cross-team technical initiatives, aligning platform, product, and infrastructure components
Translate ambiguous problem spaces into well-defined systems and execution plans
Establish engineering standards and patterns for:
- agent reliability and correctness
- evaluation and benchmarking
- observability and tracing
- safety and guardrails
Improve system performance across:
- reasoning quality
- tool-call accuracy
- latency and cost efficiency
Mentor engineers and raise the technical bar across the team
Partner with product and leadership to shape roadmap and technical strategy
Contribute to build vs buy decisions, platform direction, and long-term architecture

Required

8+ years of software engineering experience, including experience operating at a strong senior or staff level
Proven experience designing and delivering complex, distributed systems in production
Strong programming skills in Python, Go, or TypeScript
Deep experience with cloud-native architectures (AWS or GCP)
Hands-on experience building LLM-based or AI-assisted systems, including:
- prompt and context design
- tool integration and orchestration
- evaluation frameworks and metrics
- guardrails and safety mechanisms
Experience defining and operating systems with:
- high reliability and availability
- strong observability (metrics, tracing, logging)
- clear failure modes and recovery strategies
Demonstrated ability to:
- lead technical initiatives across teams
- operate effectively in ambiguous problem spaces
- balance speed of iteration with production quality

Preferred

Experience building agentic systems or multi-step reasoning workflows
Familiarity with CI/CD ecosystems (Jenkins, GitHub Actions, Argo, etc.)
Experience with LLM evaluation frameworks, tracing tools, or RAG systems
Understanding of trade-offs between autonomy, safety, and control in AI systems
Experience influencing architecture or strategy at a product or platform level

What Success Looks Like

In your first 3–6 months, you will:

Deliver AI-assisted workflows that materially reduce time-to-triage for software failures
Establish baseline evaluation frameworks for reasoning quality and system reliability
Lead the design of core agent architecture used across multiple workflows
Influence the roadmap toward safe, incremental automation

In 6–12 months:

Drive adoption of agentic systems across multiple product areas
Improve agent accuracy, reliability, and trust to production-grade levels
Establish standards and best practices used by multiple teams
Be a go-to technical leader for AI systems within the organization

Why This Role

Many AI roles focus on chat interfaces or generic copilots.

This role is different.

You’ll work on high-signal, structured problems inside real engineering systems, where:

context is fragmented and dynamic
correctness directly impacts production systems
and trust must be earned before automation is possible

You won’t just build features. You’ll help define how AI operates inside mission-critical software delivery systems.

Apply Now

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