Machine Learning Engineer

Machine Learning Engineer

Location: Bangalore (2 days/office)

Experience: 8+ Years

Employment Type: Contract

Role Overview

We are looking for an expert-level Machine Learning Engineer with strong hands-on expertise in Python to design, build, deploy, and optimize production-grade ML systems. The ideal candidate should combine deep ML knowledge with strong software engineering, system design, and MLOps capabilities.

Mandatory Skills

- 8+ years of professional experience in Machine Learning / ML Engineering.

- Expert-level Python programming skills.

- Strong understanding of Machine Learning algorithms, statistics, model evaluation, and feature engineering.

- Hands-on experience with scikit-learn, Pandas, NumPy, and ML frameworks such as PyTorch or TensorFlow.

- Strong experience building production-grade ML pipelines and inference systems.

- Solid understanding of MLOps, model deployment, monitoring, and lifecycle management.

- Strong knowledge of data structures, algorithms, software engineering, and system design.

- Experience working with SQL and large-scale datasets.

Key Responsibilities

- Design, develop, and optimize machine learning models and production ML systems using Python.

- Own the end-to-end ML lifecycle including data preparation, feature engineering, model

development, evaluation, deployment, and monitoring.

- Build scalable ML pipelines and inference services for production environments.

- Apply appropriate ML, statistical, and optimization techniques to solve complex business problems.

- Improve model accuracy, latency, scalability, and reliability through experimentation and

performance optimization.

- Collaborate with Data Scientists, Data Engineers, Software Engineers, and Product teams to

productionize ML solutions.

- Implement MLOps practices including model versioning, experiment tracking, CI/CD, monitoring, and automated deployment.

- Review technical designs and code, establish engineering best practices, and mentor other engineers.

Good-to-Have Skills

- Experience with LLMs, NLP, Generative AI, or RAG.

- Experience with PySpark / distributed ML processing.

- Knowledge of Docker, Kubernetes, and microservices.

- Experience with AWS, Azure, or GCP.

- Familiarity with MLflow, Kubeflow, Airflow, or similar ML orchestration tools.

- Experience with real-time inference, model optimization, or distributed systems.

Educational Qualifications

- Bachelor s or master s degree in computer science, Engineering, or a related technical field.

- Advanced degree or strong research/open-source contributions in ML is an advantage.