Sr Manager, AI/ML Engineering

The Opportunity:

We are looking for a Senior Machine Learning Engineer with strong technical expertise to design, build, and deploy scalable ML/AI solutions. In this role, you will be responsible for developing robust machine learning pipelines, optimizing model performance, and contributing to the design of ML/AI products that drive real business impact. You will collaborate closely with data scientists, product managers, and platform engineers to bring advanced ML capabilities into production.

Key Responsibilities:

• Lead and mentor a team of ML engineers, guiding technical decisions, best practices, and development processes. 
• Develop and train machine learning models, including feature engineering, model selection, hyperparameter tuning, and evaluation. 
• Drive the transition of ML models from proof-of-concept to production deployment. 
• Design, build, and maintain model deployment pipelines, including CI/CD workflows, model versioning, and automated testing. 
• Develop and maintain robust monitoring systems to track model performance, data drift, feature quality, and system health. 
• Design and develop backend services and APIs to integrate ML models with product systems and user-facing applications. 
• Apply strong MLOps principles to ensure reliability, reproducibility, and operational excellence of ML systems. 
• Collaborate with cross-functional teams (engineering, product, data, analytics) to deliver impactful, high-quality ML solutions

Qualifications

• Master’s degree in computer science, Machine Learning, Data Science, or related field. 
• 10+ years of experience in ML engineering, model development, or related technical roles. 
• Proven experience training, developing, and deploying ML models in real-world production environments. 
• Experience leading or mentoring engineering teams and driving complex ML projects to successful production deployment. 
• Strong experience with API development (e.g., FastAPI, Flask, Django) and backend service design. 
• Proficiency in Python and standard ML frameworks (Scikit-learn, TensorFlow, PyTorch). 
• Strong understanding of MLOps tools and practices, such as MLflow, Kubeflow, Airflow, Prefect, Docker, Kubernetes. 
• Experience building feature pipelines, data processing workflows, and optimizing model performance. 
• Experience with monitoring tools and performance alerting (e.g., Prometheus, Grafana, custom monitoring). 
• Preferred: 
• Experience with distributed computing frameworks (Spark, Ray, Dask). 
• Familiarity with microservices, event-driven architectures, or message queues. 
• Knowledge of cloud platforms (AWS/GCP) or platform-agnostic orchestration tools. 
• Experience working in a fast-paced, experimentation-driven environment.