Machine Learning Engineer

MLE - Intermediate / Senior Machine Learning Engineer (Production & MLOps Focus)

Req ID: 343719

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking a MLE - Intermediate / Senior Machine Learning Engineer (Production & MLOps Focus) to join our team in Chennai, Tamil Nādu (IN-TN), India (IN).

Job Title:

Intermediate / Senior – MLE – Python, ML Frameworks, MLOps, Containerization, Terraform, GCP, Vertex AI, IBM Watsonx

About Machine Learning Engineering at UPS Technology:

We're the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done… our innovative culture demands "yes and how" We are UPS. We are the United Problem Solvers.

Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.

About this Role:

We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You'll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS's strategic goals.

You will contribute across the full ML lifecycle—from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You'll also help shape our MLOps practices and mentor junior engineers.

Key Responsibilities:

• Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
• Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.
• Implement automated retraining, drift detection, and re-deployment of ML models.
• Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
• Implement model monitoring, observability, and alerting across accuracy, latency, and cost.
• Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.
• Ensure pipelines are secure, compliant, and cost-optimized.
• Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
• Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
• Stay current with the latest ML research and apply new tools pragmatically to production systems.
• Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.

Required Qualifications:

Education

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).

Experience

• 5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.
• Strong foundations in data structures, algorithms, and distributed systems.
• Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
• Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
• Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
• Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.
• Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).
• Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
• Strong understanding of statistical methods, ML algorithms, and deep learning architectures.

Preferred

• Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
• Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
• Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
• Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
• Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
• Domain experience in Logistics, supply chain, or large-scale consumer platforms.

About NTT DATA

NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future. Visit us at

Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client's needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use and email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form,

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