AI/ML Engineer

Machine Learning Engineer

• Design, implement, and deploy scalable ML models and pipelines for supervised & unsupervised problems, ensuring robustness and reproducibility.
• Develop production-quality Python services (APIs/batch jobs) to serve models with low latency and high throughput.
• Collaborate with data engineers and product owners to define features, data contracts, and ETL flows for reliable model inputs.
• Build and maintain CI/CD, monitoring, and automated retraining workflows (MLOps) to manage model lifecycle and drift.
• Optimize model performance and inference cost using quantization, batching, and scalable serving patterns (K8s/containers).
• Document model decisions, create test suites, and enforce engineering best practices (code reviews, unit/e2e tests, observability).
  

Skills & Qualifications

Must-Have

• 3+ years building and shipping ML models into production using Python and libraries such as TensorFlow or PyTorch.
• Hands-on experience with model serving and deployment patterns (Docker, Kubernetes, REST/gRPC endpoints).
• Solid data engineering fundamentals: SQL, ETL pipelines, experience with Spark/PySpark or equivalent.
  

Preferred

• Practical MLOps experience (MLflow,Kubeflow,Airflow) and automated CI/CD for models and data pipelines.
• Familiarity with cloud platforms (AWS/GCP/Azure) and managed ML services for scalable training/serving.
• Experience with model evaluation, A/B testing, monitoring, and techniques to mitigate drift and bias.
  

• Collaborative, engineering-driven culture with strong emphasis on ownership, code quality, and continuous learning.
• Opportunities to work on end-to-end ML products and influence architecture and go-to-market decisions.
• Competitive local compensation, regular learning stipends, and on-site collaboration with cross-functional teams.