System Engineer

LTIMindtree's interview process typically starts with a timed online aptitude test covering quantitative, logical reasoning, and verbal ability. Successful candidates move to a technical interview where they are assessed on Python programming, SQL queries, ML pipeline design, and cloud concepts. The final HR round focuses on cultural fit, communication skills, and career aspirations. Overall difficulty is moderate; practicing data‑structures, system design for ML pipelines, and cloud fundamentals helps.

LTIMindtree promotes a collaborative work culture with a strong emphasis on learning and innovation. Employees benefit from structured mentorship programs, regular tech talks, and a balanced work‑life environment that encourages flexibility and personal growth.

B.Tech/B.E. or M.Tech in Computer Science, Information Technology, Electronics, or related fields; graduating batch 2023‑2025; minimum CGPA/percentage 6.5/70%; no active backlogs at the time of joining; strong foundation in programming and databases; willingness to learn cloud and DevOps tools.

Round 1: Online Aptitude Test → Round 2: Technical Interview (ML Ops concepts, coding, system design) → Round 3: HR Interview

LTIMindtree is a global digital transformation and technology services company that blends deep industry expertise with cutting‑edge engineering capabilities. With a presence in over 30 countries, the firm helps enterprises modernize legacy systems, adopt cloud‑first strategies, and build data‑driven products. The company is known for its collaborative culture, strong focus on continuous learning, and a robust mentorship program that enables fresh talent to grow quickly. The role of a Machine Learning Operations (ML Ops) Engineer at LTIMindtree is designed for fresh graduates who are passionate about bridging the gap between data science and production systems. As an ML Ops Engineer, you will be responsible for operationalizing machine‑learning models end‑to‑end, ensuring that they are reliable, scalable, and secure. You will work closely with data scientists, data engineers, and product owners to translate experimental models into production‑ready services that deliver real business value. Key responsibilities include: 1. Designing, building, and maintaining end‑to‑end ML pipelines covering data ingestion, feature engineering, model training, validation, and deployment. 2. Implementing CI/CD workflows for automated model versioning, testing, and release using tools such as GitHub Actions, Jenkins, or Azure DevOps. 3. Managing experiment tracking and model registry with platforms like MLflow, Databricks, or SageMaker. 4. Monitoring model performance in production, detecting data drift, and triggering retraining pipelines when needed. 5. Enforcing governance, security, and access controls across the ML lifecycle. 6. Optimizing cloud infrastructure (AWS, Azure, GCP) for cost‑effective model serving using containers and orchestration tools. 7. Collaborating with cross‑functional teams to integrate models into business applications and APIs. 8. Providing incident response, root‑cause analysis, and continuous improvement of ML systems. 9. Documenting best practices, standard operating procedures, and knowledge‑base articles for the ML Ops community. 10. Staying updated with emerging ML Ops tools and industry trends to propose innovative solutions. The tech stack typically includes Python, SQL, Docker, Kubernetes, Terraform, AWS/GCP/Azure services, MLflow, Databricks, and CI/CD platforms. LTIMindtree offers a clear growth path: Junior ML Ops Engineer → ML Ops Engineer → Senior ML Ops Engineer → Lead/Architect role, with opportunities to transition into data science or cloud architecture tracks. Joining LTIMindtree means exposure to large‑scale enterprise projects, mentorship from seasoned professionals, and a supportive environment that encourages experimentation and skill development.