Machine Learning Operations (ML Ops) Engineer

LTIMindtree’s interview process typically starts with a timed online aptitude test covering quantitative, logical, and verbal sections. Shortlisted candidates face a technical interview focused on Python programming, SQL queries, cloud concepts, and MLOps tools like MLflow or SageMaker. The final HR round assesses cultural fit, communication skills, and career aspirations. Preparation should include hands‑on practice with cloud notebooks and a clear understanding of end‑to‑end ML pipelines.

LTIMindtree fosters a collaborative work culture that emphasizes continuous learning and innovation. Employees enjoy flexible work arrangements, regular skill‑upgradation programs, and a balanced focus on personal growth and project delivery.

Bachelor's or Master's degree in Computer Science, Information Technology, Electronics & Communication, or related engineering discipline. Minimum 60% aggregate (or CGPA 6.0/10). No active backlogs at the time of joining. Freshers from the 2022‑2025 batches are preferred. Strong foundation in programming, data structures, and basic machine learning concepts.

Round 1: Online Aptitude Test → Round 2: Technical Interview (ML Ops concepts, coding, cloud fundamentals) → Round 3: HR Interview (fitment, communication, expectations)

LTIMindtree is a global technology consulting and digital solutions company that blends deep industry expertise with cutting‑edge engineering. With a presence in over 30 countries, the firm helps enterprises accelerate their digital transformation journeys through services ranging from application development to AI‑driven automation. The company prides itself on a collaborative culture, continuous learning, and a strong focus on delivering measurable business outcomes for its clients. As a fresher joining LTIMindtree, you will become part of a vibrant ecosystem that encourages innovation, mentorship, and rapid skill development. The organization invests heavily in up‑skilling its talent through internal academies, certifications, and exposure to real‑world projects across sectors such as banking, healthcare, and retail. This environment not only accelerates your technical growth but also prepares you for leadership roles in the future. **Role Summary** The Machine Learning Operations (ML Ops) Engineer will be the bridge between data science experimentation and production‑grade AI solutions. You will design, build, and maintain end‑to‑end pipelines that take models from the notebook stage to scalable, monitored services that deliver value to business users. The role demands a blend of software engineering rigor, cloud‑native expertise, and a keen eye for operational excellence. **Key Responsibilities** 1. Design and implement robust ML pipelines covering data ingestion, preprocessing, model training, validation, and deployment. 2. Automate model release cycles using CI/CD tools and ensure reproducibility across environments. 3. Manage model versioning, experiment tracking, and metadata storage with platforms like MLflow or SageMaker. 4. Set up continuous monitoring for model drift, performance degradation, and infrastructure health. 5. Collaborate closely with data scientists and data engineers to translate prototypes into production‑ready services. 6. Implement security, access controls, and governance policies for ML workflows. 7. Optimize compute and storage utilization on cloud platforms (AWS, Azure, GCP) to achieve cost‑effective scaling. 8. Troubleshoot production incidents, perform root‑cause analysis, and drive continuous improvement. 9. Document pipeline architecture, operational runbooks, and best practices for knowledge sharing. 10. Explore emerging MLOps tools and recommend enhancements to the existing stack. **Tech Stack**: Python, SQL, Databricks, MLflow, SageMaker, AWS/Azure/GCP, Docker, Kubernetes, Git, Jenkins/GitHub Actions, Linux. **Growth Path**: Starting as an MLOps Engineer, you can progress to Senior Engineer, Lead Engineer, and eventually MLOps Architect or AI Delivery Manager, overseeing large‑scale AI initiatives and cross‑functional teams. **Why Join LTIMindtree**: The company offers a future‑ready environment where fresh talent works on real client problems, gains exposure to the latest AI/ML platforms, and benefits from structured mentorship programs. Competitive compensation, performance‑linked bonuses, and a clear career ladder make it an attractive launchpad for aspiring ML engineers.