Harness is led by technologist and entrepreneur Jyoti Bansal, founder of AppDynamics (acquired by Cisco for $3.7B). The company has raised ~$240M in Series E venture funding and is backed by top investors. Harness is building the industry’s leading AI-powered software delivery platform, enabling teams worldwide to build, test, and deliver software faster, safer, and more reliably. Harness brings AI and automation to the software delivery lifecycle, turning complex workflows into streamlined, scalable systems used by thousands of engineering teams globally.

The platform includes industry-leading products across CI/CD, Developer Experience, Reliability, Cloud Cost Management, and AI-driven automation. With teams across 25+ countries, Harness is shaping the future of AI-powered engineering. We’re looking for exceptional leaders to help scale this next phase.

Position Summary

The AI team builds production-grade AI agents and the core platform that powers them, including agent orchestration, prompt and tool routing, model gateways, retrieval pipelines, evaluation frameworks, observability, security, and governance. The mission is to take AI from experimentation to real production impact, embedding intelligence directly into developer workflows and the software delivery lifecycle to transform how teams build, test, deploy, secure, and operate software.

As an Engineering Manager, you will lead a small, high-impact team while remaining deeply hands-on. You will own critical systems end-to-end, contribute directly to design and implementation, run fast experimentation cycles, and convert prototypes into scalable, reliable AI products that customers use in production.

You will help drive adoption by proving value through shipped systems, setting technical direction, shaping best practices, and influencing product roadmap through real usage and outcomes. We are looking for a leader who is passionate about building great products and strong teams, thrives in ambiguity, learns fast, and balances speed with engineering rigor and production readiness.

Key Responsibilities

Engineering Leadership

• Own delivery and technical execution for production-grade AI agents and platform capabilities.
• Lead by example through hands-on contribution, technical decision-making, and strong engineering practices.
• Run rapid build–measure–learn cycles, encouraging experimentation, fast feedback, and iterative improvement.
• Translate product ideas into working systems with measurable customer impact.
• Establish lightweight processes that enable speed, quality, and accountability without slowing teams down.
• Partner closely with the product to shape scope, sequencing, trade-offs, and investment decisions.

Technical Excellence

• Remain hands-on with design, code reviews, prototyping, and critical technical implementations when needed.
• Build and operate distributed systems supporting AI workloads, pipelines, and real-time services.
• Drive quality through automated testing, observability, and operational readiness.
• Partner with engineers to debug production issues, perform root cause analysis, and improve system resiliency.
• Continuously evaluate emerging technologies, frameworks, and tools and assess their applicability to the platform.

Team Leadership

• Lead, grow, and mentor a team of high-performing engineers.
• Recruit, develop, and retain strong builders with high ownership, curiosity, and learning mindset.
• Build a team culture that values experimentation, fast learning, craftsmanship, and accountability.
• Coach engineers on both technical depth and product thinking.
• Create psychological safety to try new ideas, learn from failure, and continuously improve.

Cross-Functional Collaboration

• Work closely with Product Management, Design, Infrastructure, Security, and Go-To-Market teams.
• Collaborate with internal platform consumers and customers to understand needs and drive adoption.
• Ensure strong communication, alignment, and predictable delivery across stakeholders.
• Proactively identify risks and propose solutions rather than only highlighting problems.

Qualifications

Required Experience

• 8+ years of software engineering experience with strong system design fundamentals and hands-on delivery in production environments.
• 1-3 years of engineering leadership or technical lead experience in fast-paced product environments.
• Demonstrated ability to own and ship production systems end-to-end, from problem definition to rollout and iteration
• Working knowledge of AI-powered systems, model integration, data pipelines, or agent-based systems.
• Demonstrated ability to prototype, experiment, and iterate quickly in ambiguous problem spaces.
• Strong hands-on experience with distributed systems, cloud-native services, microservices, and APIs (REST/gRPC).
• Proficiency in at least one mainstream programming language (Java, Python, Go, or equivalent).
• Experience with CI/CD, automation, and cloud platforms (AWS/GCP/Azure).
• Strong understanding of observability, reliability, and operational excellence.

Soft Skills

• Strong ownership mindset with bias toward execution and delivery.
• Passion for building products and developing high-performing teams.
• Curiosity and continuous learning mindset with comfort in fast-evolving technical domains.
• Excellent communication and stakeholder management skills.
• Ability to influence across teams and drive alignment without authority.
• Comfortable operating in ambiguity and evolving problem spaces.
• Customer-focused with strong product intuition.

Nice to Have

• Experience building internal platforms or developer tooling, or AI-driven automation and agent systems.
• Exposure to LLM platforms, vector databases, or AI orchestration frameworks in production.
• Experience scaling systems for enterprise reliability, security, and compliance.
• Open-source contributions or strong engineering community engagement.

Work Location

Bangalore. The successful candidate will be expected to be in the Bangalore office 3x/week.

What You Will Have at Harness

• Opportunity to build foundational AI platform capabilities at enterprise scale.
• End-to-end ownership and high-impact technical leadership.
• Competitive compensation and benefits.
• Flexible work culture with strong engineering ownership.
• Learning opportunities across AI, platform engineering, and distributed systems.