Overview

Join a dynamic, high-performing team where your distinctive skills will contribute to a winning culture and strong outcomes.

Responsibilities

• Design and build ML systems end-to-end: problem framing, data prep, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
• Develop agentic AI solutions: LLM agents that plan, call tools/APIs, run multi-step workflows, and apply guardrails/fail-safes.
• Implement RAG capabilities: retrieval strategy, chunking, embeddings, indexing, and re-ranking to ground agent responses in knowledge.
• Build full-stack product experiences for ML/agent systems: backend services plus frontend UIs for configuration, human-in-the-loop review, observability, and workflow execution.
• Develop and integrate APIs/services: design and implement RESTful (and/or event-driven) integrations to serve models, agents, features, and data products.
• Build scalable data pipelines with Apache Spark (PySpark/Scala) for batch processing and feature generation.
• Use SQL extensively for exploration, transformations, validation checks, and query performance tuning on large datasets.
• Operationalize and evaluate ML/LLM systems with MLOps: CI/CD, model registry, experiment tracking, reproducible training, automated evaluation/regression tests, and quality frameworks (offline metrics + HITL).
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Qualifications

• 3+ years delivering production ML solutions, owning the end-to-end lifecycle from problem framing through deployment and iteration.
• Strong ML fundamentals, including supervised/unsupervised learning, feature engineering, model evaluation, bias/variance tradeoffs, and error analysis.
• Hands-on agentic AI / LLM application development, including tool/function calling, planning, and memory patterns.
• Implemented LLM guardrails, such as policy checks, refusal handling, PII filtering, and deterministic fallback behaviors.
• Strong Python programming skills for building ML/LLM systems and supporting tooling.
• Full-stack engineering experience, building/operating backend APIs/services (REST and/or event-driven) and modern web frontends (React/Angular/Vue) for HITL workflows, configuration, and monitoring/analytics.
• Advanced data engineering/query skills, including expert SQL (joins, window functions, CTEs, optimization), strong Apache Spark/PySpark (DataFrames, Spark SQL, tuning/partitioning), and experience running ML services with monitoring and alerting (batch or real-time).
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices