Machine Learning Engineer

Key Responsibilities

• Design and implement end-to-end ML pipelines, including automated data ingestion, preprocessing, training, evaluation, and model deployment.
• Build high-throughput, low-latency model serving APIs using Python (FastAPI/Flask) and modern serving frameworks like Triton Inference Server or TorchServe.
• Develop and maintain automated MLOps workflows using Kubeflow, MLflow, or Airflow for model tracking, lineage, and retraining.
• Maintain deployed models for data drift, concept drift, and performance degradation using observability tools like Prometheus, Grafana, or Arize AI.
• Collaborate with data engineering teams to construct scalable feature stores (e.g., Feast) using technologies such as Spark, Snowflake, or PostgreSQL.
• Write optimized, production-quality code, participating in rigorous code reviews, writing unit and integration tests, and maintaining robust CI/CD pipelines.

What We Are Looking For

• 3-6 years of experience as a Machine Learning Engineer or Software Engineer with a deep focus on production ML systems.
• Strong proficiency in Python and solid software engineering principles, including OOP, design patterns, testing frameworks, and version control (Git).
• Hands-on experience with core ML frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.
• Experience with containerization (Docker, Kubernetes) and deploying ML workloads on cloud infrastructure (AWS, GCP, or Azure).
• Solid theoretical understanding of machine learning algorithms, statistical modeling, optimization techniques, and feature engineering.
• BS, MS, or PhD in Computer Science, Data Science, Mathematics, or a highly quantitative field.
• Bonus: Experience with large-scale distributed computing (Ray, Spark), vector databases (Pinecone, Milvus), or deploying LLM inference pipelines with vLLM.