We are looking for Senior Software Engineer [Customer Facing] for our customer engineering team—it is a high-impact hybrid where hardcore DevOps/SRE problem-solving meets hands-on internal tooling and direct customer consulting.

You will troubleshoot complex, often ambiguous issues for enterprise customers across cloud and container environments—tackling broken pipelines, deployment failures, connectivity problems, and misconfigured infrastructure. You will own these issues end-to-end, acting as a trusted technical advisor to customer engineering teams, and feeding crucial findings back to our core Product and Engineering orgs..

But you won't just fix problems; you will build solutions. We put a heavy emphasis on internal development to make our entire team exponentially faster. You will engineer internal tools, diagnostic utilities, and playbooks. Crucially, you will help us build the future of our operations by developing internal AI tools on top of LLMs, training models on our own proprietary data to automate diagnostics and streamline workflows.

Please note that this is a highly customer-facing role. If you have an SRE or platform background, possess the coding chops to build robust tooling, and thrive when collaborating directly with customers on complex technical challenges, this role is a perfect fit.

• Be the Face of Harness: Serve as the primary technical resource for enterprise customers during complex troubleshooting, onboarding, and expansion.
• Code Fluency: Comfortable reading source code to understand how a product behaves, identify where something may be breaking, and form a hypothesis without waiting for core Engineering to explain it.
• Own complex customer issues across Kubernetes (k8s), ECS, Docker, cloud platforms (AWS, GCP, Azure), and on-premise/hybrid environments — from first contact through to resolution.
• Perform root-cause analysis on pipeline failures, deployment issues, runner/agent connectivity, secrets management errors, and service-to-service communication.
• Debug infrastructure automation, execution logs, and metrics data across CloudWatch, Google Cloud Operations/Stackdriver, and Azure Monitor.
• Lead incident triage during escalations, coordinate cross-functionally, and deliver clear technical findings to Engineering.
• Reproduce edge-case bugs with clean reproduction steps and drive resolution in partnership with Product and Engineering.
• Develop and maintain runbooks, troubleshooting guides, and customer-facing playbooks so solutions don't stay locked in one person's head.

Customer Engagement

• Serve as the primary technical resource for enterprise customers during onboarding, implementation, and expansion.
• Lead live troubleshooting sessions, screenshares, and technical calls — able to communicate clearly with hands-on engineers and with engineering managers who need the short version.
• Set clear expectations when issues are complex or slow-moving — customers should never be left wondering what is happening.
• Guide customers through best-practice CI/CD configurations and deployment strategies suited to their environment.

Tooling & Team Contribution

• When patterns emerge across customer issues, build scripts, utilities, or automation to address them — reducing manual effort for yourself and the team.
• Design and engineer internal applications, automation scripts, and diagnostic utilities to eliminate manual effort across the Customer Engineering team.
• AI Integration: Contribute to the development of internal AI tools powered by Large Language Models (LLMs), including training models on Harness's proprietary data to accelerate incident resolution.
• Innovate: Explore and integrate emerging AI frameworks and concepts (like Model Context Protocol / MCP servers) to enhance our internal tooling ecosystem

Required

• 4+ years in a customer-facing engineering, DevOps, SRE, or platform engineering role — where troubleshooting was a core part of the work, not an occasional side task.
• Solid hands-on experience with Kubernetes (k8s), ECS, and Docker — you can work through a broken pod, misconfigured ingress, or failed deployment without being guided step by step.
• Experience across at least two major cloud platforms (AWS, GCP, Azure), including debugging infrastructure issues, IAM/permissions, and networking problems.
• Solid understanding of CI/CD concepts and tooling — Harness, Jenkins, GitHub Actions, CircleCI, or equivalent. You need to understand how pipelines break and why; Harness-specific knowledge can be learned on the job.
• Comfortable owning a customer call without escalating every decision — able to drive a troubleshooting session, communicate progress clearly, and set honest expectations when a fix takes time.
• Able to write up technical findings clearly — for an engineer who needs the detail and for a manager who needs the summary.
• Proficiency with Linux systems, networking fundamentals (DNS, TLS, load balancing), and distributed system debugging.
• Scripting ability in at least one language (Python, Node.js, Bash, or similar) — enough to automate a diagnostic, build a small utility, or clean up a repetitive task.
• Comfortable reading source code to understand how a product behaves, identify where something may be breaking, and form a hypothesis without waiting for Engineering to explain it.
• Hands-on experience with observability tooling — Datadog, Splunk, Prometheus, or similar — for diagnosing performance issues and tracing failures across distributed systems.

Nice to Have

• Infrastructure-as-Code experience — Terraform, Pulumi, CloudFormation, or similar.
• AI & LLM Experience: Experience building internal tools on top of LLMs, fine-tuning models on custom datasets, or familiarity with RAG architectures.
• Emerging AI Ecosystems: Knowledge of AI agents and Model Context Protocol (MCP) servers.
• Experience with secrets management tools (HashiCorp Vault, AWS Secrets Manager, etc.).
• Comfortable using AI tools (e.g. GitHub Copilot, ChatGPT, or similar) to accelerate troubleshooting, write scripts, or build internal utilities.

You Might Be a Great Fit If You Are...

• A DevOps or Platform Engineer who wants to apply deep infrastructure skills in a customer-facing role.
• A Support Engineer with strong technical depth who is ready to move beyond ticket queues into hands-on engineering work.
• An SRE or Cloud Engineer who enjoys working directly with customers and solving problems that span multiple environments and teams.

Work Location

• Remote - United States/Canada Must be located in the Central or Eastern time zone*

What you will have at Harness

• Competitive salary
• Comprehensive healthcare benefits
• Flexible Spending Account (FSA)
• Flexible work schedule
• Employee Assistance Program (EAP)
• Flexible Time Off and Parental Leave
• Monthly, quarterly, and annual social and team building events
• Monthly internet reimbursement

The anticipated base salary range for this position is between $148,000 and $160,000 annually. Salary is determined by a combination of factors including location, level, relevant experience, and skills. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations.  The compensation package for this position also includes a commission/variable component, which is based on performance, plus equity, and benefits. More details about our company benefits can be found at the following link: https://www.harness.io/company/careers. 

Harness is the AI Software Delivery Platform company, led by technologist and entrepreneur Jyoti Bansal (founder of AppDynamics, acquired by Cisco for $3.7B). Harness has raised approximately $570M in funding and is valued at $5.5B, backed by leading investors including Goldman Sachs, Menlo Ventures, IVP, Unusual Ventures, Citi Ventures, and more. As AI accelerates code creation, the real bottleneck has shifted to everything after the code – testing, deployments, application security, reliability, compliance, and cost optimization. Harness brings AI and automation to this “outer loop,” helping teams ship software faster while maintaining security and governance throughout the entire software delivery lifecycle.

Powered by Harness AI and the Software Delivery Knowledge Graph, the Harness Platform applies deep context and intelligent automation across the software delivery lifecycle with governance and policy-driven controls embedded throughout the platform. 

Over the past year, Harness powered over 185M deployments, 82M builds, 18T flag evaluations, 8M security scans, 9.1B optimized tests, 3T protected API calls, and helped manage $2.8B in cloud spend — enabling customers like United Airlines, Morningstar, and Choice Hotels to accelerate releases by up to 75%, reduce cloud costs by up to 60%, and achieve 10x DevOps efficiency. 

With a global team across 14 offices and 25 countries, Harness is shaping the future of AI software delivery — and we’re looking for exceptional talent to help us move even faster.