Senior MLOps Engineer
About the Role
You will design, develop, and deploy AI and machine learning solutions for production. You will build low-latency, high-throughput data pipelines, ensure data accuracy and reliability for model training and inference, and optimize model performance and operational costs. You will integrate ML tools, evaluate new AI technologies, and embed models into applications. You will write high-quality Python code, handle noisy and unstructured data, and collaborate with data scientists and engineers to move models from experimentation to production.
Requirements
- Proficient in Python and software engineering best practices
- Experience with data stores such as ClickHouse, Elasticsearch, Postgres, Redis, and Neo4j
- Experience with orchestration tools such as Airflow, dbt, Luigi, Azkaban, or Storm
- Experience with data processing and streaming technologies such as Spark, Kafka, or Flink
- Experience with deployment and monitoring tools such as Docker, Terraform, Kubernetes, and Datadog
- Familiarity with ML integration tools like MLflow, Feast, ONNX, or Apache Beam
- Comfortable working with noisy, dirty, and unstructured data
- Strong problem-structuring and communication skills
- Experience working in cross-functional environments
Responsibilities
- Design, develop, and deploy AI and ML solutions
- Build and enhance model performance
- Ensure data accuracy, consistency, and reliability
- Build low-latency, high-throughput pipelines for real-time AI
- Integrate and evaluate new AI technologies
- Optimize operational costs for ML workloads
- Embed models into applications and production platforms
- Write high-quality code in Python
- Load, query, and transform large datasets
Benefits
- PTO
- Holidays
- Parental Leave
- Remote-first work (remote work)
- Offsites and regional meetups
- Onboarding buddies