Machine Learning Engineer - Artificial Intelligence

Job Title: Machine Learning Engineer - Artificial Intelligence

Company: Skima Innovation Private Limited

Experience: 3 - 5 Years

Skills: Machine Learning, Artificial Intelligence, Python, SQL, Deep Learning, Tensorflow, AWS, Google Cloud Platform

Description: Job Description :Machine Learning EngineerLocation : Mumbai (Andheri East), India (In-Office)Experience : 3+ YearsAbout 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: 3+ years of hands-on experience in ML engineering with a focus on production-grade deployments.- 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. .