• Connect technology talent to real-world projects. • Work in healthy environments with end-to-end professional growth opportunities. • Collaborate with other professionals and leadership on relevant, high-impact projects.

Role Description We are seeking an experienced Data & AI Engineer to support the execution of our packaged QuickStarts, particularly those focused on GenAI (LLM Ops) and Data Lake architecture. This role will lead the development and deployment of scalable, production-ready AI/ML solutions built on AWS-native technologies. The ideal candidate thrives in a fast-paced, consulting environment and is comfortable navigating ambiguity while leading client-facing delivery. They are a proactive leader who asks the right questions, anticipates challenges, and drives clarity, while collaborating effectively across product, DevOps, and architecture teams. Above all, the ideal candidate embodies RevStar’s core values: - Self-Mastery: We hold a high bar for how we think, communicate, and improve. - Ownership: We own outcomes, not just effort. - Shared Destiny: We rise or fall together. Responsibilities - Design, build, and deploy LLM-based APIs using Amazon Bedrock, supporting models like Claude, Titan, Nova, and Cohere. - Implement retrieval-augmented generation (RAG) pipelines using OpenSearch + S3. - Build data pipelines for curated and raw data layers using AWS Glue, Athena, and Lake Formation. - Support real-time reporting and AI usage dashboards via Amazon QuickSight. - Design serverless workflows using AWS Lambda and manage prompt metadata with DynamoDB. - Implement prompt orchestration strategies, including fallback handling, versioning, and safety guardrails. - Collaborate with product managers, architects, and DevOps to align AI/ML delivery with customer and project goals. - Document solutions clearly and support onboarding for handoff to client teams. - Stay current with AWS innovations in GenAI, MLOps, and scalable data platforms. Qualifications - Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field. - 5+ years of experience designing, building, and deploying cloud-based ML or GenAI systems. - Deep hands-on experience with AWS AI/ML services: Amazon Bedrock, AgentCore, SageMaker, Lambda, S3, DynamoDB, Glue, Athena, Lake Formation, OpenSearch, and QuickSight. - Strong Python development skills, with a focus on data manipulation, API development, and automation. - Experience implementing retrieval-augmented generation, prompt orchestration, or LLMOps workflows preferred. - Familiarity with CI/CD, Infrastructure-as-Code (IaC), and Git-based workflows. - Excellent communication skills and ability to explain AI system design and tradeoffs to both technical and business stakeholders in a client-facing, professional services environment. Benefits - Paid Time Off – Take the time you need to recharge and stay productive. - Remote-First Working Environment – Collaborate from anywhere while staying connected with our global team. - Comprehensive Health Coverage – Medical, Dental, Vision. - 401(k) Retirement Plan – Plan for your future with access to a company-sponsored 401(k) program. - Annual Learning & Development Stipend – Invest in your skills with conferences, certifications, or courses. - Peer Mentorship & Coaching – Learn from experienced engineers, product managers, and architects to accelerate your growth. - Professional Growth Opportunities – Exposure to cutting-edge AWS GenAI, data, and cloud technologies across diverse industries. - Company Outings & Volunteer Opportunities – Build relationships and give back to the community. - Collaborative, Innovative Culture – Work alongside top talent in a fast-paced, supportive environment that values curiosity and initiative.

• Design and implement scalable data ingestion and transformation pipelines using Databricks and cloud platforms • Lead architecture decisions for modern data platforms, including Medallion Architecture and Lakehouse patterns • Build and maintain ETL/ELT pipelines using Python and SQL, following engineering best practices • Lead and mentor engineering teams, coordinating deliveries and driving technical standards • Establish and evolve CI/CD pipelines and Infrastructure as Code (IaC) practices for data platforms • Partner with clients and stakeholders to translate business requirements into scalable data solutions • Champion data governance, quality, and security using tools such as Unity Catalog • Contribute to platform modernization initiatives, including Data Warehouse to Lakehouse migrations • Apply Agile methodologies to plan, iterate, and deliver data engineering projects effectively

• Build and grow customer-facing data products • Build AI-powered and agentic analytics tools • Build the backend services, APIs, and query layer that power these products • Partner with product and design to translate customer and business needs into reliable, performant, data-intensive features