Robots & Pencils

Data Engineer

• Design, implement, and deploy data and AI/ML systems on AWS end-to-end, including data lake pipelines, observability instrumentation, and optimization for performance, reliability, and cost
• Maintain and evolve production platforms, monitor for drift, debug issues, and drive improvements to reliability and scalability
• Use AI-forward coding tools such as Claude and Cursor to ship higher-quality work
• Partner with AWS Professional Services, client engineering, and data teams
• Translate technical tradeoffs, system behavior, and constraints for non-specialists
• Participate in code reviews and design discussions
• Contribute to data and AI architecture decisions
• Own meaningful work end-to-end and support more junior engineers

Senior Data Engineer

AI Engineer

• Define AI system architecture and lead design across LLM applications, agentic systems, retrieval, and ML model serving for end-to-end engagements
• Drive RAG and context-engineering strategy, including hybrid retrieval, reranking, chunking, and embedding model selection across vector stores
• Lead multi-agent orchestration design using frameworks such as LangGraph, Amazon Bedrock Agents, or AgentCore
• Own model serving and inference architecture on AWS, optimizing latency, throughput, and cost with streaming, caching, batching, and quantization
• Define LLMOps and MLOps standards across engagements, including prompt versioning, evaluation pipelines, CI/CD, and feature stores
• Drive evaluation, guardrails, and observability strategy, establishing offline and online evaluation, LLM-as-judge scoring, and tracing
• Own responsible AI posture, including governance, PII and data isolation, prompt-injection defense, and grounding controls
• Lead decisions on fine-tuning, prompting, RAG, and tool use while weighing accuracy, cost, and maintainability
• Use AI-forward tools such as Claude and Cursor to ship higher-quality work at pace
• Partner with leadership and clients on AI technical direction and translate business goals into architectural decisions
• Communicate complex AI concepts and tradeoffs to engineering and non-engineering stakeholders
• Collaborate with engineering, data, ML, and product teams
• Develop and maintain AI architecture documentation, evaluation standards, and reusable patterns
• Establish architectural standards and best practices across the team
• Mentor junior and mid-level engineers
• Act as a technical escalation point for complex architectural and integration challenges
• Evaluate emerging technologies and recommend tools, frameworks, and patterns

Staff AI Architect

• Design and implement scalable data pipelines, taking ownership of features end-to-end
• Build and manage data warehouses and data lakes
• Optimize data processing workflows and integrate data from multiple systems
• Implement data modeling and schema design that supports analytics and downstream use
• Implement data validation and monitoring to ensure data quality, reliability, and availability
• Design and build data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows
• Use tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace
• Collaborate with analytics, AI, and product teams to align data work with broader product and business goals
• Communicate technical tradeoffs and decisions clearly across functions
• Participate actively in code reviews and design discussions
• Contribute to data architecture decisions
• Take ownership of meaningful work end-to-end, including operating data systems
• Support data governance and compliance efforts

• Build and ship AI/ML and LLM-powered system features for production
• Implement and maintain AI/ML and AI agent pipeline components from data ingestion through model deployment
• Contribute to LLM-powered features including prompts, evaluations, retrieval, and tool integrations
• Handle data preprocessing, feature engineering, and basic evaluation to validate model and prompt performance
• Collaborate with engineers across a distributed, cross-functional team
• Document experiments, results, and model decisions
• Participate in code reviews and team discussions
• Take ownership of assigned tasks and deliver them end-to-end
• Share experiment and research learnings with the broader team

Technical Product Manager

• Define product strategy and vision for major client engagements, aligned with client goals and business outcomes
• Own the product roadmap end-to-end, prioritizing business value, technical constraints, and delivery timelines
• Lead complex discovery and problem framing with senior client stakeholders
• Define product success metrics and use data to evaluate outcomes, inform priorities, and drive continuous improvement
• Drive cross-functional alignment across engineering, design, delivery, and strategy
• Use AI tools such as Claude for product discovery, research synthesis, and documentation
• Build and maintain trusted advisor relationships with client leadership
• Communicate strategy, tradeoffs, and recommendations at the executive level
• Lead stakeholder working sessions, workshops, and decision-making forums
• Establish product delivery standards and practices
• Mentor senior and mid-level product managers
• Act as a technical escalation point for complex product, scope, and prioritization challenges

Staff Technical Product Manager

• Design, implement, and deploy ML/AI models end-to-end from concept through production
• Build data pipelines and training workflows
• Optimize models for performance, accuracy, and efficiency
• Maintain and evolve production AI systems
• Monitor for drift and debug production issues
• Drive ongoing improvements to reliability and scalability
• Use AI-forward coding tools such as Claude and Cursor
• Partner with product, engineering, and data teams to align AI work with product and business goals
• Translate technical tradeoffs, model behavior, and constraints for non-specialists
• Participate in code reviews and design discussions
• Contribute to AI architecture decisions
• Own meaningful work end-to-end, including productionization
• Raise engineering standards and support more junior engineers

Senior AI Engineer

• Define data architecture and platform strategy across pipelines, warehouses, and data lakes
• Build and optimize scalable batch and real-time data pipelines
• Define and enforce data governance, quality standards, and compliance frameworks
• Build monitoring, logging, and alerting for data pipelines and services
• Contribute to CI/CD workflows for data deployment and automation
• Drive data platform modernization for performance, cost, and scalability
• Use AI assistants such as Claude and Cursor to improve delivery quality and speed
• Design and implement data contracts and event flows with backend, platform, and engineering teams
• Lead data pipelines for production AI/ML systems, including embeddings, vector stores, RAG preparation, feature stores, and training/inference flows
• Integrate data services with APIs, middleware, and third-party systems
• Partner with leadership on data strategy
• Collaborate with engineering, analytics, AI, and product teams
• Advocate for data quality, governance, and platform best practices
• Establish data engineering standards across the team
• Mentor junior and mid-level engineers
• Make high-stakes architectural decisions and manage long-term tradeoffs

Staff Data Engineer

Vice President

• Define AI and digital strategy for major client engagements, owning the opportunity portfolio from discovery through scale roadmaps
• Lead complex, multi-stakeholder strategy engagements independently, driving alignment across executive, operational, and technical audiences
• Build and present board- and executive-level business cases with clear tradeoffs, value quantification, and pragmatic recommendations
• Design and lead evaluation strategy to improve AI system effectiveness, accuracy, and calibration across client programs
• Create reusable strategy accelerators including playbooks, reference architectures, evaluation templates, and agent patterns
• Define AI governance and responsible AI frameworks suited to clients' regulatory contexts, risk postures, and operational realities
• Build agent workflows, conversational experiences, and AI-enabled demonstrations
• Serve as a senior strategic partner to client leadership and build long-term trusted relationships
• Lead executive briefings, strategic planning workshops, and multi-stakeholder alignment forums
• Establish strategic delivery standards and ways of working
• Mentor senior and mid-level analysts and help grow their strategic thinking, client skills, and delivery impact
• Contribute to business development, proposals, and growth of the strategy practice
• Act as an escalation point for complex scope, prioritization, and strategic framing challenges

VP, Strategist

Artificial Intelligence

• Lead the design and delivery of AI/ML systems
• Define AI architecture and lead model development and optimization
• Design and build agentic workflows, including reasoning loops, tool/function calling, and single- and multi-agent orchestration
• Build and maintain RAG pipelines with chunking, embeddings, OpenSearch vector search, re-ranking, and refresh handling
• Integrate AWS Bedrock and Agent Core, including MCP-based tool design
• Write and iterate on production system prompts
• Build evaluation and observability into agents using golden datasets, RAGAS-style metrics, LLM-as-judge, and tracing
• Design for LLM failures using retries, backoff, circuit breakers, fallback models, and user-facing degradation handling
• Build backend services in Python and Node.js, including AWS Lambda and API Gateway serverless architectures
• Design DynamoDB single-table schemas for conversation state, agent memory, and session history
• Support event-driven orchestration with Step Functions, SQS, and EventBridge
• Contribute to frontend integration points and write clean, tested code across the stack
• Support deployment, monitoring, and production troubleshooting in AWS and Docker environments
• Partner with product, design, and delivery leads across global teams to scope and ship features end-to-end
• Mentor engineers and improve agentic engineering practices, including use of Claude Code
• Own features and releases end-to-end, including debugging, hardening, and production reliability

Staff AI Builder

Cloud Engineer

• Define technical strategy and lead architectural design across cloud, data, and AI/ML systems for end-to-end engagements
• Architect scalable cloud-native solutions, including multi-cloud and hybrid strategies
• Design data architectures including warehouses, data lakes, and batch and streaming pipelines
• Design AI/ML systems including model serving, MLOps pipelines, feature stores, and LLM-based applications
• Define infrastructure-as-code, CI/CD, and DevOps standards
• Drive performance, scalability, cost, and reliability optimization
• Ensure architecture meets security, governance, and compliance requirements
• Lead cloud migrations and platform modernization initiatives
• Use AI-forward tools such as Claude and Cursor to deliver quality work efficiently
• Partner with leadership and clients on technical direction and translate business goals into architectural decisions
• Communicate complex technical concepts to engineering and non-engineering stakeholders
• Collaborate with engineering, data, AI, and product teams
• Develop and maintain architecture documentation, standards, and guidelines
• Establish architectural standards and best practices
• Mentor junior and mid-level engineers
• Act as a technical escalation point for complex architectural and integration challenges
• Evaluate emerging technologies and recommend tools, frameworks, and patterns

Staff Cloud Architect – L4

• Contribute to architectural design across cloud, data, and AI/ML systems, taking ownership of meaningful components
• Build and integrate cloud-native solutions including microservices, serverless, and containerized workloads
• Implement infrastructure as code and CI/CD pipelines
• Design and contribute to data architectures including pipelines, warehouses, and modeling
• Support AI/ML system design, including model serving, MLOps pipelines, and integration of LLM-based capabilities
• Implement monitoring, logging, and observability across deployed systems
• Collaborate with engineering, data, AI, and product teams to translate requirements into technical solutions
• Communicate technical tradeoffs and design decisions across functions
• Maintain architecture documentation
• Participate in design reviews
• Take ownership of system components with growing architectural responsibility
• Contribute to architectural standards and best practices
• Mentor junior engineers and support their growth

Senior Cloud Architect

Security Engineer

• Define security architecture and engineering standards across web, API, and cloud-native systems, leading the hardening of complex services
• Lead threat modeling and security design reviews across applications and AI systems, setting how risk is identified and prioritized
• Architect cloud security controls across identity, network, and data layers, owning IAM strategy, secrets management, and encryption
• Lead the integration of security automation into CI/CD pipelines, driving shift-left detection and policy-as-code across teams
• Lead detection engineering, logging, and incident response practices across production systems
• Own the security of AI and LLM-powered applications in production, defining defenses against prompt injection, data leakage, model abuse, and agentic-AI threats
• Lead the design and integration of AI services into production security workflows, including LLM APIs, tool and function calling, and multi-step agent flows for detection and triage
• Use tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace
• Partner with product, engineering, and leadership to shape security strategy and align technical direction with business outcomes
• Translate complex security tradeoffs, risks, and opportunities into clear narratives for technical and non-technical stakeholders
• Lead security reviews and technical discussions, raising engineering rigor across the team
• Engage with engineering, data, and client teams to align security work with business priorities and measurable outcomes
• Define security architecture and engineering standards, including responsible AI practices
• Mentor and grow junior and mid-level engineers through coaching, code reviews, and pairing
• Own ambiguous, high-stakes work through production with attention to reliability, cost, and safety

Senior Data Engineer

Staff AI Architect

Senior Data Engineer

AI Engineer

Staff Technical Product Manager

Senior AI Engineer

Staff Data Engineer

VP, Strategist

Staff AI Builder

Staff Cloud Architect – L4

Senior Cloud Architect

Staff Security Engineer