Machine learning engineer

Machine Learning Engineer

Location: Mumbai (Andheri East), India (In-Office)

Experience: 1 to 5 Years (Multiple roles available)

About Skima Innovation:

At Skima, we don't just build models; we build the future. We are a dynamic team dedicated to

pushing the boundaries of what's possible through data-driven innovation. We are looking for a

talented Machine Learning Engineer who is ready to take ownership of end-to-end ML lifecycles and

transform complex data into scalable, real-world solutions.

The Role:

As an ML Engineer at Skima, you will sit at the intersection of data science and software engineering.

You won't just be "playing with data"you will be designing, developing, and deploying high-

performance models that drive our core products. You will work in a collaborative environment where

your algorithms directly impact business outcomes.

Key Responsibilities

Production Pipelines: Architect and manage automated ML implementation pipelines for

seamless transition from research to production.

Deep Learning Deployment: Optimize and deploy large-scale Deep Learning models using

specialized inference engines.

Containerization & Orchestration: Package ML services using Docker and manage

deployments via Kubernetes to ensure high availability and scalability.

MLOps Mastery: Establish CI/CD for ML, implementing automated testing, versioning

(DVC), and model registry workflows.

Model Observability: Implement comprehensive monitoring for model drift, data integrity,

and real-time performance latency.

Optimization: Fine-tune models for resource efficiency, focusing on quantization and pruning

for production-grade inference.

What You Bring:

Experience: 1 to 5 years of hands-on experience in ML engineering with a focus on production-

grade deployments. (Multiple roles available)

MLOps Stack: Proficiency with tools like MLflow, Kubeflow, W&B for managing the model

lifecycle.

Cloud & Infrastructure: Strong experience with AWS/Azure/GCP ML services and

containerized environments.

Technical Depth: Expert-level Python and deep familiarity with PyTorch or TensorFlow.