Ready to build the future with AI?

At Genpact, we don’t just keep up with technology—we set the pace. AI and digital innovation are redefining industries, and we’re leading the charge. Genpact’s AI Gigafactory, our industry-first accelerator, is an example of how we’re scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to 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 advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions – we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today. Get to know us at genpact.com and on LinkedIn, X, YouTube, and Facebook.

Inviting applications for the role of Assistant Manager, ML Ops Engineer

We are seeking a highly skilled and experienced ML Ops / LLM Ops Engineer to join our team. You will play a crucial role in building and maintaining the infrastructure and pipelines for our cutting-edge Generative AI applications, working closely with the Generative AI Full Stack Architect. Your expertise in automating and streamlining the ML lifecycle will be instrumental in ensuring the efficiency, scalability, and reliability of our Generative AI models in production.

Responsibilities

Design, develop, and implement ML/LLM pipelines for generative AI models, encompassing data ingestion, pre-processing, training, deployment, and monitoring.

MLOps Support and Maintenance on ML Platforms Dataiku/Sagemaker

Apply understanding of Dataiku Govern functionalities, including item and artifact management, workflow control, and sign-off processes.

Using best practices for data governance and model accountability within the MLOps lifecycle.

Automate ML tasks across the model lifecycle, leveraging tools like GitOps, CI/CD pipelines, and containerization technologies (e.g., Docker, Kubernetes).

Implement version control, CI/CD pipelines, and containerization techniques to streamline ML and LLM workflows.

Design and implement monitoring and alerting systems to track model performance, data drift, and other key metrics.

Conduct ground truth analysis to evaluate the accuracy and effectiveness of LLM outputs compared to known, correct data.

Work closely with infrastructure and DevOps teams to provision and manage resources for ML and LLM development and deployment.

Develop and maintain robust monitoring and alerting systems for generative AI models in production, ensuring proactive identification and resolution of issues.

Collaborate with the Generative AI Full Stack Architect and other engineers to optimize model performance and resource utilization.

Manage and maintain cloud infrastructure (e.g., AWS, Azure) for ML workloads, ensuring cost-efficiency and scalability.

Stay up to date on the latest advancements in MLOps and incorporate them into our platform and processes.

Communicate effectively with technical and non-technical stakeholders about the health and performance of generative AI models.

Qualifications we seek in you

Minimum Qualifications

Bachelor’s degree in computer science, Data Science, Engineering, or a related field, or equivalent experience.

Experience in MLOps or related areas, such as DevOps, data engineering, or ML infrastructure.

Knowledge of best practices for data governance and model accountability within the MLOps lifecycle.

Covered these Dataiku Certifications: ML Practitioner Certificate, Advanced Designer Certificate and MLOps Practitioner Certificate.

Proven experience in ML Ops, LLM Ops, or related roles, with hands-on experience deploying and managing machine learning and large language model pipelines Expertise in cloud platforms (e.g., AWS, Azure) for ML workloads.

Strong understanding of CI/CD principles and containerization technologies like Docker and Kubernetes.

Familiarity with monitoring and alerting tools for ML systems (e.g., Prometheus, Grafana).

Excellent communication, collaboration, and problem-solving skills.

Ability to work independently and as part of a team.

Passion for Generative AI and its potential to revolutionize various industries.

Why join Genpact?

Lead AI-first transformation – Build and scale AI solutions that redefine industries

Make an impact – Drive change for global enterprises and solve business challenges that matter

Accelerate your career—Gain hands-on experience, world-class training, mentorship, and AI certifications to advance your skills

Grow with the best – Learn from top engineers, data scientists, and AI experts in a dynamic, fast-moving workplace

Committed to ethical AI – Work in an environment where governance, transparency, and security are at the core of everything we build

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.