Full Stack Engineer (ML)

As a Full Stack ML Engineer, you will be responsible for designing, developing, and scaling end-to-end machine learning solutions for enterprise-grade applications. This is a hands-on role requiring strong expertise across the ML lifecycle—from data processing and model development to deployment and production monitoring.

You will work on building deep learning models (Neural Networks using PyTorch), developing scalable data pipelines, and ensuring robust deployment using modern MLOps practices on Azure/Databricks platforms. The role demands strong ownership in delivering production-ready ML systems and collaborating closely with engineering and business teams.

Key Responsibilities

• Design, develop, and deploy end-to-end ML solutions from data ingestion to production inference.

• Build and train deep learning models using PyTorch and neural networks.

• Develop scalable data pipelines using Python, SQL, and PySpark.

• Implement and manage model tracking, versioning, and registry (MLflow or similar).

• Deploy and expose models via APIs/services for real-time and batch inference.

• Establish CI/CD pipelines for ML workflows using GitHub and cloud-native tools.

• Implement model monitoring, drift detection, retraining pipelines, and alerting mechanisms.

• Work with Azure, Databricks, and CosmosDB for scalable ML infrastructure.

• Containerize applications using Docker and orchestrate via Kubernetes (as needed).

• Optimize ML pipelines and infrastructure for performance, scalability, and cost efficiency.

• Collaborate with data engineering teams to integrate large-scale data pipelines.

• Troubleshoot production issues including performance bottlenecks, failures,

and data inconsistencies.

• Work closely with business stakeholders to ensure ML solutions align with objectives.

Required Skills and Experience

• Strong hands-on experience in Python programming.

• Expertise in deep learning frameworks (PyTorch preferred).

• Strong understanding of Neural Networks and model training at scale.

• Proficiency in SQL and PySpark for data processing.

• Experience in building and deploying end-to-end ML systems.

• Strong knowledge of MLOps practices (CI/CD, monitoring, retraining).

• Experience with Azure ecosystem and Databricks.

• Familiarity with GitHub workflows, version control, and collaboration practices.

• Experience with API-based model deployment.

• Understanding of distributed systems and large-scale ML architectures.

• Experience working with structured and unstructured data (Parquet, Delta, JSON).

Being a Mathemagician

• Embody MathCo’s culture and way of working.
• Demonstrate ownership and strive for excellence in delivering results.
• Actively engage and contribute to initiatives fostering company growth.
• Support diversity and appreciate different perspectives.