5+ years of hands-on experience in applied data science, machine learning, AI engineering, or AI research
Computer Science or equivalent technical degree strongly preferred
Strong Python skills and practical experience with data and AI libraries/platforms such as Databricks, and LLM APIs, scikit-learn
Experience building and shipping ML or GenAI applications—from early prototype through usable internal or customer-facing workflows
Deep familiarity with modern LLM ecosystems, including OpenAI, Anthropic/Claude, Hugging Face, and open-weight models
Ability to select models and design effective LLM applications using prompting, context management, structured outputs, retrieval, and tool use
Experience building agentic or multi-step AI workflows with LangGraph, LangChain, Semantic Kernel, or similar orchestration frameworks
Strong evaluation mindset: defining useful quality metrics, building representative evaluation datasets, assessing reliability, and making data-driven tradeoffs
Comfortable working with large, messy, structured, and unstructured data to produce features, insights, and clear visualizations
Proficiency with Git, testing, code review, and collaborative software-development practices
Practical, balanced judgment: comfortable exploring emerging AI capabilities while building maintainable, secure, dependable systems
Proactive and accountable, with strong written and verbal communication skills across technical and non-technical partners
Strong MLOps experience, including MLflow or comparable tooling, experiment tracking, reproducible pipelines, model/application versioning, CI/CD, serving, and production monitoring
Experience operating ML or GenAI systems at scale, including observability, tracing, incident response, and data or model-drift detection
Experience with Databricks ML, AWS SageMaker, Azure ML, or similar managed ML platforms
Familiarity with MCP, agent-tool integrations, LLM guardrails, and production safety practices
Experience with AI-assisted development tools such as Copilot, Claude Code, or Codex
Exposure to cybersecurity, fraud detection, anomaly detection, code analysis, or software supply-chain security
Experience with PySpark and production data pipelines
Experience working within a software product company or SaaS