MLOps Engineer

At Happening.xyz, we are looking for a hands-on MLOps Engineer to build, scale, and maintain the infrastructure supporting our machine learning models. You’ll focus on developing efficient, reusable training and inference pipelines, optimizing model serving, and improving the ML experimentation lifecycle. This role bridges the gap between Data Science and scalable infrastructure, empowering teams to transition models seamlessly from development to production.

What We’re Looking For:

An MLOps Engineer who excels in building scalable ML infrastructure. You’ll design robust, production-ready ML pipelines, ensure code reusability, and optimize workflows. Strong candidates will have a good understanding of ML operations, data engineering, and best practices for model deployment and automation.

Key Responsibilities:

• ML Pipeline Development: Design, build, and maintain scalable training and inference pipelines. Automate workflows with orchestration tools like Airflow or MLflow.
• Model Serving & Infrastructure: Deploy and scale models using efficient frameworks (e.g., TF Serving, BentoML), optimizing for latency and throughput.
• Data Engineering: Collaborate with Data Engineers to build efficient ETL pipelines and ensure data availability for model training and real-time inference.
• Code Optimization & Reusability: Refactor ML code for modularity and reusability, ensuring that data scientists can focus on ML without worrying about infrastructure.
• Monitoring & Observability: Implement logging, monitoring, and alerting systems to track model performance and ensure operational efficiency.
• Collaboration: Work closely with data scientists to ensure smooth transitions from model development to production.

Key Skills & Qualifications:

• ML Pipelines: Experience designing scalable and efficient training and inference pipelines.
• Cloud & Infrastructure: Proficient with AWS, Kubernetes, and Docker for deploying and managing ML models.
• Programming: Strong Python skills for ML infrastructure and automation; familiarity with Java for frameworks like Apache Flink is a plus.
• Data Engineering: Experience with Snowflake, SQL optimization, and building efficient data pipelines (Airflow, Spark, etc.).
• Tools: Familiarity with tools like MLflow, DVC, and feature stores (e.g., Feast) for model versioning and experimentation tracking.
• Scalability & Performance: Ability to design infrastructure and pipelines that scale and ensure low-latency model inference.

Bonus Points If You Have:

• Experience with streaming data and event-driven architectures (e.g., Kafka, Flink).
• Knowledge of CI/CD for ML and data pipeline automation.
• Experience building self-service data tools for data scientists.

What We Offer:

• Competitive Salary & Benefits: Comprehensive package that values your contributions.
• Healthcare & Pension: Private medical insurance, pension contributions (6% employer/3% employee).
• Impactful Work: A collaborative, dynamic environment where you’ll play a key role in enhancing ML workflows.

Join us at Happening.xyz and help build the infrastructure that powers cutting-edge machine learning solutions!