Architect - DevOps 4D

Ready to turn bold ideas into real-world impact?

At Genpact, we don’t just adapt to change, we lead it. AI and digital innovation are transforming the way businesses work, and we’re at the forefront of it. Genpact’s AI Gigafactory, our industry-first accelerator, exemplifies how we scale advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. Whether tackling complex challenges through large-scale models or agentic AI, our breakthrough solutions tackle companies’ most complex challenges.

If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what’s possible, this is your moment.

Genpact (NYSE: G) is an agentic and advanced technology solutions company. We leverage process intelligence and artificial intelligence to deliver measurable outcomes. With a strong partner ecosystem and decades of client trust, we provide innovative solutions that transform how businesses run. Powered by a team with an active learning mindset and client centricity at its core, we deliver lasting value for the world’s leading enterprises.

Get to know us at genpact.com and on LinkedIn, YouTube, X, and Facebook.

Job Description

Role

We are looking for a DevOps engineer to own CI/CD for our ML and GenAI applications on Azure and Databricks. Infrastructure is already provisioned and managed by a separate platform team - your focus is the delivery pipeline, deployment automation, and the observability and guardrail layer around models and LLM applications in production.

Key Responsibilities

• Build and maintain CI/CD pipelines in GitHub Actions for Python applications, ML models, and GenAI/LLM services.

• Implement branching, versioning, and artifact promotion across DEV, SIT, and PROD environments.

• Automate deployment of Databricks jobs, notebooks, and workflows using Databricks Asset Bundles and the Databricks CLI.

• Automate model deployment to Databricks Model Serving and Azure ML online endpoints, including canary rollout and rollback.

• Integrate MLflow into pipelines for experiment tracking, model registry in Unity Catalog, and stage promotion.

• Add automated quality gates to pipelines: unit tests, linting, container scanning, model validation, and LLM evaluation checks.

• Implement LLM evaluation harnesses in CI - golden datasets, RAGAS-style retrieval metrics, LLM-as-judge, and regression tests that block bad prompt or model changes.

• Configure guardrails for GenAI applications: input/output filtering, PII redaction, prompt-injection and jailbreak checks, Azure AI Content Safety.

• Set up observability for models and LLM applications - MLflow Tracing, OpenTelemetry, Azure Monitor / Application Insights - covering latency, token usage, cost, tool calls, and failure modes.

• Configure monitoring and alerting for data drift, model performance degradation, and endpoint health.

• Manage secrets, service principals, and OIDC-based authentication between GitHub Actions and Azure/Databricks.

• Containerise applications with Docker and deploy to existing AKS or Azure Container Apps environments.

• Work with data scientists and GenAI engineers to move use cases from notebook to production reliably.

Required Skills & Experience

• 6-10 years in DevOps, platform, or ML engineering roles.

• Strong hands-on experience building CI/CD with GitHub Actions - reusable workflows, matrix builds, environments, approvals, self-hosted runners.

• Solid Python skills, plus Bash, YAML, and Git.

• Hands-on experience with Databricks: Workflows, Unity Catalog, MLflow, Model Serving, Asset Bundles, Databricks CLI.

• Working knowledge of Azure: Azure ML, ADLS Gen2, Key Vault, ACR, AKS or Container Apps, Entra ID service principals, Azure Monitor.

• Practical MLOps experience: model packaging, registry, versioning, automated retraining, drift and performance monitoring.

• Practical LLMOps experience with production GenAI applications - RAG and/or agent frameworks such as LangChain or LangGraph.

• Hands-on experience implementing LLM guardrails and evaluation, not theoretical familiarity.

• Experience with Docker and container-based deployment.

• Experience with observability tooling and instrumenting applications for tracing and metrics.

Nice to Have

• Azure or Databricks certification.

• Experience in a regulated industry with model governance or audit requirements.

• Exposure to Langfuse or LangSmith, and to inference cost optimisation.

• Familiarity with Azure DevOps pipelines alongside GitHub Actions.

Qualifications

Certifications

Required Skills

Language

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Why join Genpact?

• Lead AI-powered transformation - Drive innovation and solve real-world business challenges that matter
• Make an impact - Help global enterprises solve business challenges that matter
• Accelerate your career - Gain hands-on experience, mentorship, and world-class learning opportunities to stay ahead
• Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day
• Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the 140,000+ coders, tech shapers, and growth makers at Genpact and take your career in the only direction that matters: Up. Let’s build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a 'starter kit,' paying to apply, or purchasing equipment or training.