Machine Learning Engineer

Role Overview

Bridge the gap between theoretical ML models and production-grade services. You’ll design and optimize inference pipelines, build scalable feature stores, and automate model retraining loops for high-volume prediction workloads, working closely with platform engineers and data scientists.

Responsibilities

• Design, optimize, and deploy high-throughput ML pipelines using PyTorch or TensorFlow in Docker/Kubernetes environments.
• Build and maintain feature engineering workflows with PySpark, SQL, and feature stores such as Feast to ensure training-serving consistency.
• Implement model monitoring, logging, and alerting for data drift, concept drift, and latency.
• Create automated CI/CD and MLOps pipelines using MLflow, Kubeflow, or Argo Workflows for model promotion and rollback.
• Integrate models into microservice architectures via high-performance gRPC or REST APIs.
• Profile and optimize runtime performance using techniques like quantization, distillation, and TensorRT.

Requirements

• 3–6 years of professional software engineering or ML engineering experience, including shipping ML to production.
• Strong Python skills, including pandas, NumPy, and scikit-learn, plus PyTorch or TensorFlow.
• Hands-on experience with AWS or GCP and managed ML services (SageMaker or Vertex AI).
• Familiarity with Docker, Kubernetes, and database design (relational and/or non-relational).
• BS or MS in Computer Science, Data Science, Math, or a related quantitative field.

Nice to Have

• Vector databases
• PEFT (parameter-efficient fine-tuning)
• Deploying LLMs with frameworks like vLLM