Role: Senior Systems Engineer
Industry Type: IT Services & Consulting
Department: Data Science & Analytics
Employment Type: Full Time, Permanent
Role Category: Data Science & Machine Learning
UG: Any Graduate
Job Title: ML Software Engineer / MLOps Engineer (Infrastructure & Production Support)
Location: [Insert Location/Remote]
Experience Level: Mid-Senior (47 years)
Position Overview
We are looking for an ML Software Engineer (ML SWE) with strong ML Infrastructure capabilities to join our team. In this role, you will not just build models, but ensure they run reliably, efficiently, and accurately in production. You will be responsible for tackling technical debt, clearing backlog items driven by user issues, and performing deep-root cause analysis on model performance drops or pipeline failures.
This role is ideal for a software engineer who loves machine learning and thrives on optimizing existing systems, fixing complex bugs, and stabilizing production ML environments.
Core Responsibilities
- Production Support & Bug Fixing: Diagnose, troubleshoot, and resolve complex issues across the ML lifecycle (data pipelines, training workloads, and inference services).
- Technical Debt Reduction: Refactor existing ML codebases and infrastructure to improve maintainability, scalability, and code quality.
- Model Tuning & Optimization: Debug model performance issues, conduct root cause analysis on model drift or degradation, and apply model tuning techniques to stabilize performance.
- Backlog Management: Own and deliver on prioritized backlogs arising from end-user feedback, system alerts, or operational inefficiencies.
- Infrastructure Management: Maintain and optimize ML platforms, orchestration layers, and CI/CD pipelines for machine learning workflows.
Required Skills & Qualifications
- Programming Mastery: Strong proficiency in Python (and/or Java depending on customer confirmation) with an emphasis on writing clean, production-grade, object-oriented code.
- Data Science Capabilities: Solid understanding of machine learning fundamentals, model evaluation metrics, hyperparameter tuning, and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- ML Infra & Orchestration: Experience with ML pipelines and orchestration tools (e.g., MLflow, Kubeflow, Airflow) and data versioning (DVC).
- DevOps Foundation: Hands-on experience with containerization (Docker, Kubernetes) and Cloud Infrastructure (AWS, Azure, or GCP).
- Analytical Mindset: Exceptional root-cause analysis and debugging skillsùthe ability to trace an issue from a bad data input all the way through to a skewed model prediction.