Description

Required: Strong foundation in ML/AI (statistics, probability, optimization) with the ability to apply these concepts to real-world problems. 5+ years of experience building, deploying, and operating data and ML systems in production. Proficient in Python, Java, and SQL; strong software engineering fundamentals (system design, testing, version control, code reviews). Hands-on experience with workflow orchestration and data pipelines (e.g., Airflow, Kubeflow) and cloud data platforms/storage (e.g., SageMaker Feature Store, Snowflake, DynamoDB, OpenSearch). Experience with the ML lifecycle and MLOps tooling (e.g., MLflow, Metaflow, SageMaker; LLM/agent frameworks such as LangChain/LangGraph; model evaluation/observability tools such as Galileo or similar). Working knowledge of containerization and cloud infrastructure, including Docker and Kubernetes, GitOps/CI/CD tools (e.g., Argo CD), and at least one major cloud platform (AWS, Google Cloud Platform, or Azure). Understanding of data modeling and scalable systems, including distributed computing and streaming frameworks (e.g., Spark/EMR, Flink, Kafka Streams); familiarity with GPU-based implementation is a plus. Demonstrated ability to ramp up quickly and operate effectively in new application/business domains. Strong written and verbal communication skills: able to document and present designs and decisions, and comfortable giving/receiving feedback in an Agile environment.

 Desired: Familiarity with ML problem areas and techniques, including recommendation systems (e.g., graph-based approaches, two-tower models), time-series modeling (classical and deep learning), representation learning (e.g., embeddings), anomaly detection, and causal inference. Practical experience with LLMs and generative AI workflows, including foundation model fine-tuning, RAG, and vector databases. Evidence of technical leadership/impact, such as contributions to open-source data/ML projects and/or published technical presentations, blog posts, papers, or research. Domain experience (plus) in communications, marketing automation, or customer engagement analytics. Familiarity with AI-assisted development tools (e.g., Claude, GitHub Copilot/Codex, Cursor, etc.). Advanced degree preferred (M.S. or Ph.D.) in a relevant field.

Key Skills

Education

Any Graduate