Machine Learning Engineer

About Company

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They balance innovation with an open, friendly culture and the backing of a long-established parent company, known for its ethical reputation. We guide customers from what’s now to what’s next by unlocking the value of their data and applications to solve their digital challenges, achieving outcomes that benefit both business and society.

About Client:

Our client is a global digital solutions and technology consulting company headquartered in Mumbai, India. The company generates annual revenue of over $4.29 billion (₹35,517 crore), reflecting a 4.4% year-over-year growth in USD terms. It has a workforce of around 86,000 professionals operating in more than 40 countries and serves a global client base of over 700 organizations. Our client operates across several major industry sectors, including Banking, Financial Services & Insurance (BFSI), Technology, Media & Telecommunications (TMT), Healthcare & Life Sciences, and Manufacturing & Consumer. In the past year, the company achieved a net profit of $553.4 million (₹4,584.6 crore), marking a 1.4% increase from the previous year. It also recorded a strong order inflow of $5.6 billion, up 15.7% year-over-year, highlighting growing demand across its service lines. Key focus areas include Digital Transformation, Enterprise AI, Data & Analytics, and Product Engineering—reflecting its strategic commitment to driving innovation and value for clients across industries.

JD: Senior Machine Learning Engineer EXP-10+ Yrs Core Focus: Python-first ML engineering with strong Infrastructure‑as‑Code (Terraform) and

production deployment experience on GCP. Key Responsibilities & Skills Python & ML Engineering Expert‑level Python with strong OOP and functional programming skills Hands‑on experience building, testing, and optimizing production‑grade ML code Proficiency with ML/DL libraries: TensorFlow, PyTorch, scikit‑learn, pandas, NumPy, PySpark Strong understanding of end‑to‑end model lifecycle: training, versioning, deployment, and monitoring Infrastructure as Code & Automation Strong hands‑on experience with Terraform for provisioning and managing GCP infrastructure Automating ML platforms, pipelines, and environments using IaC Experience with Docker for containerized ML workloads Familiarity with Kubernetes (GKE) is a plus

Cloud & ML Platforms (GCP) Experience using Vertex AI for model training, deployment, and lifecycle management Working knowledge of GCP services such as BigQuery, Cloud Storage, Cloud Run, Pub/Sub, Dataproc, and Dataflow Solid understanding of GCP IAM and VPC concepts MLOps & CI/CD Building and maintaining ML pipelines using Vertex AI Pipelines, Airflow, or similar tools CI/CD experience using GitHub, Jenkins, and/or GCP Cloud Build Monitoring and observability for deployed ML models API Development & System Design Designing and building RESTful APIs using FastAPI or Flask for real‑time inference Integrating ML models into scalable, fault‑tolerant services Experience with microservices, distributed systems, and asynchronous processing