Senior MLOps Engineer

Role Description

It is a remote job for a 3-month contract (extendable). Design and operate production-grade ML systems that are reliable, scalable, and fully observable.
Own the transition of models from experimentation to high-throughput services.
Prevent model decay and reduce data science operational load through automation and monitoring.

Qualifications

• 5+ years in Data Science/ML with 3+ years in MLOps or Production Engineering
• Proven experience deploying and maintaining production ML services
• Strong Python and SQL expertise
• Experience working with cloud platforms (AWS, Azure, or GCP)
• Background integrating ML with business systems (ERP, CRM, Supply Chain)
• Experience collaborating with Data Engineering and DevOps teams
• Ability to build resilient systems in legacy or imperfect environments

Responsibilities

• Architect and deploy ML models across the full lifecycle
• Implement monitoring for data drift, model drift, and performance degradation
• Build automated retraining pipelines and rollback mechanisms
• Develop CI/CD workflows for model versioning and reproducibility
• Optimize inference performance and reduce latency & compute costs
• Collaborate with teams to operationalize models for business applications

Must Have

• Expert-level Python and API development (FastAPI/Flask)
• Containerization & orchestration experience (Docker, Kubernetes)
• Experience with ML pipelines & orchestration tools (Airflow, Prefect, Dagster)
• Hands-on model monitoring & observability implementation
• Experience deploying real-time or high-throughput inference systems

Nice to Have

• Experience with MLflow, DVC, Kubeflow, or Weights & Biases
• Knowledge of model optimization (quantization, pruning, caching)
• Experience with Prometheus, Grafana, or advanced monitoring tools
• Familiarity with Spark, Databricks, or Snowflake ecosystems
• Experience with SageMaker, Vertex AI, or Azure ML platforms

Job Type: Full-time

Work Location: Remote