Machine Learning Engineer

About The Role The role owns the end-to-end lifecycle of machine learning and deep learning systems, translating cutting-edge AI research into robust, scalable production services. The team collaborates closely with data engineers and product stakeholders to build high-throughput models where inference latency, accuracy, and system resilience are paramount. Key Responsibilities Architect and deploy scalable machine learning pipelines using Python, PyTorch, and modern MLOps tooling Develop high-performance inference APIs using FastAPI or gRPC to serve models with strict latency SLAs Optimize model architectures for production via quantization, pruning, and distributed training techniques Build automated monitoring pipelines to track data drift, concept drift, and system health metrics Collaborate with infrastructure teams to manage containerized deployments using Docker, Kubernetes, and Terraform Conduct thorough code reviews and contribute to internal standards for ML reproducibility and experimentation What We Are Looking For 3 to 6 years of professional experience in software engineering, with at least 3 years focused specifically on machine learning engineering Strong proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch or TensorFlow Demonstrated track record of deploying and maintaining ML models in production environments using AWS, GCP, or Azure Solid understanding of distributed data processing tools like Spark or Ray, and vector databases like Pinecone or Milvus BS or MS in Computer Science, Machine Learning, Statistics, or a related quantitative field Bonus: Experience fine-tuning large language models or building agentic RAG architectures