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.