About the position
Responsibilities
- Lead ML Engineering to architect, build and deploy production grade GenAI services and solutions.
- Work on large-scale stateful and stateless distributed systems, including infrastructure, data ingestion platforms, SQL and no-SQL databases, microservices, orchestration services and more.
- Lead MLOps/LMOps platform development & automated pipelines focusing on deploying, monitoring and maintaining models in production environments; with model governance, cost and performance optimization.
- Collaborate with cross-functional teams to integrate machine learning models into production systems.
- Create and manage Documentation and knowledge base, including development best practices, MLOps/LLMOps processes and procedures.
- Work closely with members of technology teams in the development, and implementation of Enterprise AI platform.
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 8+ years of progressive experience in machine learning, data analytics or similar roles.
- 5 years of relevant experience with writing production level, scalable code with Python (or Scala).
- Experience in MLOps/LLMOps, machine learning engineering, Big Data, or a related role.
- Proficiency with Elasticsearch, SQL, NoSQL, Apache Airflow, Apache Spark, Kafka, Databricks, MLflow.
- Experience with containerization, Kubernetes, cloud platforms, CI/CD and workflow orchestration tools.
- Experience in distributed systems programming, AI/ML solutions architecture, Microservices architecture.
Nice-to-haves
- 2-3 years of experience with operationalizing data-driven pipelines for large scale batch and stream processing analytics solutions.
- Experience with contributing to open-source initiatives or in research projects and/or participation in Kaggle competitions.
- 6-12 months of experience working with RAG pipelines, prompt engineering and/or Generative AI use cases.
- Experience with SageMaker and/or Vertex AI.
Benefits
- Health care coverage designed for the mind and body.
- Generous time off to keep you energized.
- Access to resources for continuous learning and career growth.
- Competitive pay and retirement planning options.
- Family-friendly perks and benefits for partners and children.
- Retail discounts and referral incentive awards.
Tags & focus areas
Used for matching and alerts on DevFound Remote Lead Machine Learning Kubernetes Scala Python Spark Airflow Mlflow Fulltime