ML Engineer: Build & Deploy End-to-End AI Pipelines

JD:
Primary

Skills:

Python, Machine Learning, Tensor Flow, PyTorch, MLOps, AWS/Azure/GCP, Docker, Kubernetes

Required Skills

• Strong Python expertise with Num Py, Pandas, Scikit-learn.

• Hands-on experience with Tensor Flow and/or PyTorch.

• Experience building and deploying ML solutions on AWS, Azure, or GCP.

• Knowledge of ML pipelines, model evaluation, CI/CD, version control, and MLOps practices.

• Strong understanding of ML system design, performance optimization, and monitoring.

Preferred Skills

• Experience with MLflow, Sage Maker, Azure ML or similar MLOps platforms.

• Knowledge of Docker, Kubernetes, Spark, or Ray.

• Experience with LLMs, Deep Learning, Transformers, Vector Databases, and ML Governance.

• Prior experience leading ML projects or mentoring teams.

Key Responsibilities

• Design, build, deploy, and maintain ML models and end-to-end ML pipelines.

• Perform data preparation, feature engineering, model training, evaluation, and optimization.

• Deploy and monitor models in production, including model drift detection and retraining.

• Collaborate with data engineering, Dev Ops, and business teams to deliver scalable ML solutions.

• Architect enterprise-scale ML systems and drive MLOps, automation, governance, and reliability initiatives.

• Mentor engineers, provide technical leadership, and evaluate emerging AI/ML technologies.