Machine Learning Engineer - Python

As a Machine Learning Engineer at our company, your role involves designing, building, and deploying scalable ML/AI solutions that align with business objectives. You will be responsible for developing APIs, microservices, and feature pipelines for production ML systems. Additionally, you will build and manage end-to-end MLOps pipelines for training, validation, deployment, and monitoring. Your tasks will also include optimizing ML models for performance, latency, and scalability, as well as collaborating with cross-functional teams to translate requirements into production solutions. Furthermore, you will implement model governance, versioning, testing, and monitoring best practices, and collaborate on ML infrastructure, cloud, and DevOps architecture. Some of your key responsibilities will include: - Designing, building, and deploying scalable ML/AI solutions aligned with business objectives - Developing APIs, microservices, and feature pipelines for production ML systems - Building and managing end-to-end MLOps pipelines (training, validation, deployment, monitoring) - Optimizing ML models for performance, latency, and scalability - Working with cross-functional teams to translate requirements into production solutions - Implementing model governance, versioning, testing, and monitoring best practices - Collaborating on ML infrastructure, cloud, and DevOps architecture - Mentoring junior engineers and contributing to technical roadmap and best practices To qualify for this role, you should have: - A Bachelors/Masters/PhD in Computer Science or a related field - Strong experience in building and deploying ML models for batch/streaming/real-time use cases - Proficiency in Python; working knowledge of Java/Scala - Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow) - Experience with Docker, Kubernetes, and major cloud platforms (AWS/GCP/Azure) - Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn, TensorRT, CUDA) - Experience with data processing tools (Spark, Kafka) and databases (SQL/NoSQL/VectorDB) - Exposure to monitoring tools like Prometheus and Grafana Preferred qualifications include: - Experience with distributed systems and large-scale ML production environments - Open-source contributions or community participation - Strong communication and collaboration skills - Self-starter with problem-solving mindset and willingness to learn In addition to these requirements, we offer competitive compensation, stock options, health insurance, and the opportunity to work on cutting-edge ML systems. As a Machine Learning Engineer at our company, your role involves designing, building, and deploying scalable ML/AI solutions that align with business objectives. You will be responsible for developing APIs, microservices, and feature pipelines for production ML systems. Additionally, you will build and manage end-to-end MLOps pipelines for training, validation, deployment, and monitoring. Your tasks will also include optimizing ML models for performance, latency, and scalability, as well as collaborating with cross-functional teams to translate requirements into production solutions. Furthermore, you will implement model governance, versioning, testing, and monitoring best practices, and collaborate on ML infrastructure, cloud, and DevOps architecture. Some of your key responsibilities will include: - Designing, building, and deploying scalable ML/AI solutions aligned with business objectives - Developing APIs, microservices, and feature pipelines for production ML systems - Building and managing end-to-end MLOps pipelines (training, validation, deployment, monitoring) - Optimizing ML models for performance, latency, and scalability - Working with cross-functional teams to translate requirements into production solutions - Implementing model governance, versioning, testing, and monitoring best practices - Collaborating on ML infrastructure, cloud, and DevOps architecture - Mentoring junior engineers and contributing to technical roadmap and best practices To qualify for this role, you should have: - A Bachelors/Masters/PhD in Computer Science or a related field - Strong experience in building and deploying ML models for batch/streaming/real-time use cases - Proficiency in Python; working knowledge of Java/Scala - Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow) - Experience with Docker, Kubernetes, and major cloud platforms (AWS/GCP/Azure) - Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn, TensorRT, CUDA) - Experience with data processing tools (Spark, Kafka) and databases (SQL/NoSQL/VectorDB) - Exposure to monitoring tools like Prometheus and Grafana Preferred qualifications include: - Experience with distributed systems and large-scale ML production environments - Open-source contributions or community participation - Strong communication and collaboration skills - Self-starter with problem-solving mindset and willingness to learn In addition t