Senior Machine Learning Engineer

About Company :

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 whats now to whats 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 Engineeringreflecting its strategic commitment to driving innovation and value for clients across industries.

JD: Senior Machine Learning Engineer

Core Focus: Python-first ML engineering with strong InfrastructureasCode (Terraform) and production deployment experience on GCP.

Key Responsibilities & Skills

Python & ML Engineering

Expertlevel Python with strong OOP and functional programming skills

Handson experience building, testing, and optimizing productiongrade ML code

Proficiency with ML/DL libraries: TensorFlow, PyTorch, scikitlearn, pandas, NumPy, PySpark

Strong understanding of endtoend model lifecycle: training, versioning, deployment, and monitoring

Infrastructure as Code & Automation

Strong handson 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 realtime inference

Integrating ML models into scalable, faulttolerant services

Experience with microservices, distributed systems, and asynchronous processing