Senior Machine Learning Engineer

Location: Bengaluru

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 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
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:
Tensor Flow, PyTorch, scikit‑learn, pandas, Num Py, Py Spark
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 Big Query, 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 Git Hub, 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