Machine Learning Engineer

Job Description We are seeking a skilled Machine Learning Engineer to design, build, and optimize scalable ML and GenAI-powered applications. You will work across the ML lifecycle—from data ingestion and model training to deployment and monitoring—leveraging distributed computing, orchestration frameworks, and microservices in a cloud or hybrid environment. Key Responsibilities: • Design and implement scalable ML pipelines using Python and distributed data processing frameworks. • Develop and maintain microservices and REST APIs for model serving and data access. • Build and orchestrate ETL and ML workflows using Apache Airflow. • Collaborate with data scientists and product teams to deploy ML/GenAI models into production. • Optimize performance of ML workloads using GPUs and cloud-native tools. • Ensure reliability, observability, and scalability of ML systems in production. Qualifications: • Python (5+ years): Strong software engineering skills with a focus on clean, maintainable code. • Python Libraries (2–4 years): Proficiency with pandas, NumPy, scikit-learn, TensorFlow or PyTorch. • Machine Learning (2–4 years): Experience building, training, and deploying ML models. • Apache Spark (2+ years): Hands-on experience with distributed data processing. • Apache Airflow (2+ years): Building and managing DAGs for data and ML workflows. • Microservices (2+ years): REST API development and integration for ML model serving. Preferred Skills: • Cloud Platforms (2+ years): Experience with AWS, GCP, or Azure for scalable ML deployments. • GenAI / LLMs / GPUs (2+ years): Familiarity with Large Language Models, CUDA, TensorRT, or GenAI frameworks. • Experience with containerization (Docker, Kubernetes) and CI/CD pipelines. • Knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.). Nice to Have: • Exposure to vector databases (e.g., FAISS, Pinecone, Weaviate). • Experience with prompt engineering or fine-tuning LLMs. • Contributions to open-source ML/AI projects.