Sr. ML Engineer – ML & Applied AI

About the role

We are seeking a Senior Machine Learning Engineer with 10+ years of experience to design, build, and scale production-grade machine learning and AI systems that power data-driven decision making across the enterprise.

Responsibilities

• Architect and build scalable, production-grade ML systems from experimentation to deployment and lifecycle management
• Design and implement end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, and inference
• Develop and maintain high-performance model serving systems using APIs (e.g., FastAPI) for real-time and batch inference
• Lead the design and implementation of feature stores and reusable feature pipelines across teams
• Build and optimize distributed data processing workflows using Spark, Databricks, or similar platforms
• Implement and enforce MLOps best practices, including CI/CD pipelines, automated retraining, model versioning, and experiment tracking
• Design and manage model monitoring and observability frameworks to track performance, drift, latency, and system health
• Drive strategies for model retraining, drift detection, and continuous improvement
• Collaborate closely with data engineers, platform teams, and product stakeholders to integrate ML solutions into production systems
• Contribute to the adoption of modern AI capabilities, including LLMs, vector databases, retrieval-augmented generation (RAG), and agentic workflows
• Ensure high standards of code quality, testing, documentation, and reproducibility

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• 10+ years of experience in machine learning, software engineering, or related roles, with significant experience in production ML systems
• Strong programming expertise in Python and solid software engineering fundamentals (data structures, system design, APIs)
• Extensive experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow
• Proven experience designing and deploying scalable ML pipelines and services in production
• Hands-on experience with model serving frameworks and API development (e.g., FastAPI, Flask)
• Strong experience with containerization (Docker) and orchestration platforms such as Kubernetes
• Experience working with cloud platforms (GCP, AWS, or Azure) and building cloud-native ML solutions
• Deep understanding of ML lifecycle management, including training, evaluation, deployment, monitoring, and retraining
• Experience implementing CI/CD pipelines for ML workflows and managing version control systems (Git)
• Strong experience with SQL and distributed data processing frameworks (e.g., Spark, PySpark)
• Excellent problem-solving skills and ability to design scalable, maintainable systems