Spigot Software - Senior MLOps Engineer

We are looking for a Senior MLOps Engineer to drive the design, deployment, and scalability of machine learning and LLM systems in production. This role focuses on end-to-end ML lifecycle management, production-grade ML infrastructure, and close collaboration with Data Science, Backend, and Cloud teams to ensure reliable, secure, and high-performing ML solutions.

Key Responsibilities :

MLOps & ML Lifecycle :

- Design, build, and maintain end-to-end MLOps pipelines covering model training, validation, deployment, monitoring, and retraining.

- Productionize machine learning and LLM models, ensuring reliability, scalability, and low latency.

- Implement model versioning, experiment tracking, and reproducibility.

- Monitor model performance, data drift, and system health in production environments.

ML Platform & Infrastructure :

- Build and maintain ML infrastructure on cloud platforms (Azure preferred, AWS acceptable).

- Deploy ML workloads using Docker and Kubernetes (EKS/AKS).

- Develop and manage CI/CD pipelines for ML workflows and model releases.

- Support high-availability, fault-tolerant ML systems in production.

Backend & Integration :

- Develop and maintain Python-based services and APIs (Flask/Django/FastAPI) to serve ML models.

- Integrate ML pipelines with data platforms, feature stores, and downstream systems.

- Work closely with Backend and Architecture teams to ensure seamless ML system integration.

Collaboration & Ownership :

- Partner with Data Scientists, DevOps, QA, Security, and Product teams to move models from research to production.

- Take ownership of design initiatives, from architecture decisions to execution.

- Implement security, data privacy, and governance best practices across ML systems.

Requirements :

- Must-Have 5+ years of experience in Python development, with significant exposure to MLOps or ML systems.

- Strong experience with ML deployment and production workflows.

- Hands-on expertise with Docker, Kubernetes, and CI/CD pipelines.

- Experience working with cloud platforms (Azure preferred, AWS acceptable).

- Solid understanding of SQL, relational databases, and performance optimization.

- Prior collaboration with Data Science teams to productionize ML models.

- Fluency in English (written and verbal).

Good to Have :

- Experience with ML libraries and data frameworks (scikit-learn, pandas, PySpark, PyArrow).

- Exposure to big data technologies (Snowflake, Spark).

- Familiarity with LLM workflows and inference optimization.

- Experience implementing monitoring, logging, and observability for ML systems.

Soft Skills :

- Strong problem-solving and analytical mindset.

- Proactive, independent, and ownership-driven. Excellent collaboration and communication skills.

- Adaptable and comfortable in fast-paced environments.

- Continuous learner with a passion for ML systems.