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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, developing, and deploying scalable AI/ML and GenAI systems, with a strong focus on production-quality code and analytical modeling techniques. Proven ability to mentor junior data scientists and collaborate effectively with cross-functional teams to drive AI productization.
Highest-signal resume keywords
AI/ML System Design And DevelopmentGenAI Solutions And Prompt EngineeringStatistical Analysis And Hypothesis TestingMLOps/LLMOps PracticesPython Programming And Production Code
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI/ML System DesignGenAI SolutionsStatistical AnalysisDeep LearningTensorFlowPyTorchPython ProgrammingMLOpsSQLLarge-Scale Data Processing
Soft Skills
Strong Communication SkillsMentoringCollaboration
Tools & Technologies
GitDockerKubernetesSparkHiveBigQuery
Industry Keywords
Production AI/ML SystemsAgentic AI FrameworksEvaluation StrategiesContinuous Quality MonitoringEngineering Excellence
Tech Stack
Tools & technologiesBigQueryDockerKubernetesPythonPyTorchSparkSQLTensorflow
About the role
Key responsibilities & impact- Translate and co-own business problems into data-driven or mathematical solutions
- Own end-to-end design, development, deployment, and operation of production AI/ML and GenAI systems
- Build agentic AI workflows supporting multi-step reasoning, tool use, planning, and autonomous task execution
- Architect and operate stateful AI systems with conversation state, long-term memory, context persistence, and retrieval strategies
- Drive GenAI and ML evaluation through offline benchmarks, online A/B experimentation, and continuous quality monitoring
- Uphold trustworthy AI practices, including guardrails, human-in-the-loop processes, monitoring, governance, and safety mechanisms
- Identify AI/ML and agentic AI use cases and build MVPs and proofs of concept
- Select analytical modeling techniques, develop custom models, conduct EDA, statistical analysis, hypothesis testing, inference, and experiments
- Develop and revise features based on model responses
- Identify evaluation metrics and apply model testing and tuning techniques
- Support deployment, evaluation, monitoring, and sustainability of analytical models in production
- Write production-quality code and deliver application features
- Lead technical design discussions and write design documents and RFCs
- Participate in technical interviews and hiring
- Mentor junior data scientists and contribute to design and code reviews
- Partner with product, engineering, and platform teams to shape AI productization and associate-builder solutions
Requirements
What you’ll need- 6+ years of professional experience designing, developing, deploying, and maintaining scalable, production-grade AI/ML and GenAI systems
- Bachelor's or Master's degree in computer science, engineering, statistics, mathematics, economics, or a related quantitative field
- Strong industry experience building production AI/ML systems
- Hands-on experience with production GenAI solutions, including RAG, advanced prompt engineering, and orchestration patterns
- Familiarity with agentic AI frameworks such as LangChain and LangGraph
- Experience managing conversational context, memory, state, retrieval strategies, session management, and personalization
- Experience with LLM/GenAI evaluation strategies, offline benchmarks, online A/B experimentation, red-teaming, guardrails, and continuous quality monitoring
- Deep knowledge of statistics, classical machine learning, and deep learning
- Extensive hands-on use of TensorFlow, PyTorch, or equivalent frameworks
- Experience building, optimizing, deploying, and performance-tuning deep learning systems in production
- Expertise in Python and production-quality code
- Experience with MLOps/LLMOps, including CI/CD pipelines, model registries, feature stores, monitoring, drift detection, and online experimentation
- Experience with Git, Docker, and Kubernetes
- Working knowledge of SQL and large-scale data processing tools such as Spark, Hive, or BigQuery
- Ability to mentor junior data scientists and collaborate with organizational stakeholders
- Strong communication skills and experience presenting technical concepts, research findings, and product insights
- Commitment to engineering excellence, automation, standards, and continuous improvement
- Minimum qualification alternatives: bachelor's degree plus 3 years' analytics experience; master's degree plus 1 year's analytics experience; or 5 years' experience in an analytics or related field
Benefits
Comp & perks- Incentive awards for performance
- Maternity and parental leave
- Paid time off (PTO)
- Health benefits
- Compensation package
- Flexibility for associates to manage their personal lives