Sr Data Scientist II

Key Responsibilities

• Architect modular agentic applications with clear separation among retrieval, prompt construction, model invocation, tool execution, state and history management, orchestration, validation, and response formatting.
• Independently refactor complex or legacy Python code to improve correctness, readability, modularity, extensibility, testability, and runtime performance.
• Own production readiness for AI components, including input validation, exception handling, timeout management, retries with backoff, fallback behavior, configuration management, and secure handling of credentials.
• Establish observability for LLM and retrieval workflows through structured logging, metrics, distributed tracing, alerting, and actionable error reporting.
• Design clear interfaces and data contracts between retrieval, orchestration, model, and downstream application components.
• Write comprehensive unit, integration, regression, and end-to-end tests, including tests for failure modes, malformed model responses, empty retrieval results, and unavailable dependencies.
• Review Python and agentic application code, identify architectural and operational risks, and provide actionable feedback aligned with production engineering standards.
• Diagnose and optimize latency, memory usage, retrieval performance, token consumption, model cost, and application scalability.
• Apply appropriate data structures, algorithms, and computational-complexity analysis when designing and optimizing solutions.
• Participate in production deployments, incident investigation, root-cause analysis, remediation, and continuous reliability improvements.

Required Qualifications

• Advanced Python proficiency demonstrated through independently designing, implementing, debugging, testing, reviewing, and refactoring production applications.
• Strong command of Python fundamentals, standard data structures, common algorithms, object-oriented and functional design principles, type annotations, and time and space complexity analysis.
• Demonstrated ability to transform prototype or experimental code into modular, maintainable, observable, and production-ready systems.
• Strong understanding of software design principles, including separation of concerns, dependency injection, interface design, configuration management, and effective abstraction.
• Experience implementing automated unit, integration, regression, and end-to-end testing using tools such as pytest, including appropriate mocking of external services.
• Experience designing resilient distributed applications that account for timeouts, retries, rate limits, partial failures, malformed responses, idempotency, and graceful degradation.
• Experience with production observability, including structured logging, metrics, tracing, alerting, and incident troubleshooting.
• Demonstrated ability to conduct rigorous code reviews and identify correctness, maintainability, performance, security, testing, and operational risks.
• Experience taking technical ownership of applications across their lifecycle, from design and experimentation through deployment, monitoring, incident response, and ongoing improvement.
• Strong understanding of production LLM concerns, including structured output validation, context management, model and tool failures, prompt versioning, token and cost controls, security, and evaluation.

Preferred Qualifications

• Experience with Python quality tooling such as pytest, ruff, mypy, profiling tools, and automated CI quality gates.
• Experience defining typed schemas and validating LLM inputs and outputs using tools such as Pydantic.
• Experience building evaluation frameworks for agentic systems, including task-completion, retrieval-quality, groundedness, hallucination, latency, reliability, and cost metrics.
• Experience implementing model fallbacks, tool-use controls, guardrails, human-in-the-loop workflows, and auditability for AI applications.
• Experience supporting production services and participating in incident response, root-cause analysis, and post-incident remediation.
• The successful candidate will:
• Demonstrate senior-level Python proficiency and sound computer-science fundamentals.
• Treat correctness, maintainability, testing, resilience, security, and observability as core design requirements.
• Recognize architectural issues and improve code beyond simply making it functional.
• Independently review and refactor complex agentic application code.
• Make clear engineering tradeoffs involving quality, latency, scalability, reliability, and cost.
• Take end-to-end ownership from experimentation through production deployment and operational support.
• Communicate technical decisions and code-review feedback clearly and constructively.
• Combine strong LLM and retrieval expertise with disciplined software-engineering practices.

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Working Pattern

About the Business