Senior – MLE – Python, ML Frameworks, MLOps, Containerization, Terraform, GCP, Vertex AI, IBM W[...]

UPS; Chennai District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

UPS is seeking a Senior Machine Learning Engineer to design, develop, and deploy ML models that drive business outcomes. This role involves working closely with data scientists and software engineers to build scalable ML systems.

Key responsibilities include maintaining production-ready ML models, adopting MLOps best practices, and mentoring junior engineers. The candidate must have over 5 years of experience in ML engineering and a strong foundation in Python and cloud environments.

Qualifications

• 5+ years of experience in machine learning engineering, MLOps, or large‑scale AI/DS systems.
• Hands-on experience building and deploying ML models at scale in cloud environments.

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.

Skills

Machine Learning Engineering

MLOps

Data Structures and Algorithms

Python

SQL

Education

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field

Tools

TensorFlow

PyTorch

Docker

Kubernetes

Terraform

GitHub Actions

Job description

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. 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 Qualifications

• 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.

Employee Type

Permanent

UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.