Jobs at RevStar Consulting

Lead pre-sales technical discovery and serve as the primary architectural authority for enterprise AWS AI/ML, data, and cloud modernization initiatives. Design Bedrock and SageMaker solutions, migration strategies, and modern data platforms; develop AWS cost models, SOWs, proposals, and funding inputs; evaluate risks and compliance requirements; and coordinate technical handoffs with delivery teams. The role partners with account executives, AWS field teams, customers, and internal engineers without carrying a sales quota.

Own AWS co-sell opportunities from partner referral through contract closure. Build relationships with AWS account teams, qualify cloud modernization and application opportunities, lead business discovery, maintain accurate HubSpot and AWS ACE pipeline data, coordinate proposals and SOWs with Solutions Architects, and develop enterprise account expansion strategies. The role requires B2B technology sales experience and familiarity with partner-led channel ecosystems, with AWS and incentive-program experience preferred.

Architects and manages cloud-native AI infrastructure, containerized workloads, serverless environments, and automated CI/CD pipelines. Builds Terraform-based infrastructure, implements observability, security controls, compliance automation, and cost optimization. Leads client discovery workshops, technical demonstrations, requirements gathering, cloud adoption roadmaps, and enterprise delivery while communicating complex architectures to stakeholders.

Lead enterprise AWS consulting engagements from architecture through deployment. Design resilient cloud-native, serverless, and containerized solutions; migrate legacy infrastructure; and deliver Generative AI and machine learning workloads using AWS services. Partner with executive stakeholders, build proofs of concept, drive cloud adoption and account growth, mentor engineers and architects, and develop reusable Terraform modules, Well-Architected Review frameworks, and delivery best practices.

Lead enterprise delivery of Databricks-based data, machine learning, and Generative AI solutions. Design production data pipelines, analytics engines, RAG applications, and ML platforms; migrate legacy data estates to the Lakehouse; and advise executive client stakeholders. Drive platform adoption, identify expansion opportunities, mentor technical teams, and develop reusable engineering accelerators. The role combines hands-on architecture, engineering, consulting, customer engagement, and technical practice building.

Design and evolve modular game engines, visual templates, UI systems, and gameplay frameworks for interactive learning modules. Responsibilities include abstracting graphics and mechanics into configurable systems, establishing UI/UX and visual standards, defining asset pipeline specifications, conducting design and usability reviews, advising clients, and documenting style guides and technical configuration parameters.

Lead hands-on software engineering across delivery teams by writing production code, governing architecture, reviewing code, and establishing standards for ReactJS, NodeJS, and AWS systems. Build reusable engineering accelerators, drive AI adoption, conduct project audits, and develop technical training. Mentor senior engineers and collaborate with product, DevOps, and QA leaders to align technical decisions with business outcomes.

Lead end-to-end AWS migration engagements from discovery through stabilization. Manage technical delivery, client relationships, scope, risks, timelines, escalations, team allocations, and delivery governance. Collaborate with Solutions Architects, Engineering, DevOps, QA, Product, Sales, and clients on migration strategies, dependency mapping, backlog refinement, estimation, cutover planning, quality gates, and follow-on modernization opportunities.

Own end-to-end product delivery for enterprise clients, from discovery and roadmap definition through launch. Lead client workshops, create PRDs and prioritized backlogs, guide Agile cross-functional teams, manage Azure DevOps governance, and communicate with executives. Use generative AI and rapid-prototyping tools to validate experiences and accelerate delivery. Serve as the primary client contact while managing scope, risks, technical tradeoffs, and account growth.

Design, build, and deploy production-ready LLM APIs and GenAI solutions on AWS. Implement RAG pipelines, data lake pipelines, serverless workflows, prompt orchestration, and AI dashboards. Collaborate with product, DevOps, and architecture teams, document solutions, and support client handoffs while staying current with AWS GenAI and MLOps innovations.

Design and build responsive React frontends and scalable Node/Express backends. Implement search and ETL pipelines, integrate data sources on AWS, translate Figma designs into reusable UI components, optimize performance, collaborate in Agile teams, and engage with clients for requirements and demos.

Design, build, and optimize Databricks-based data pipelines and Lakehouse architectures. Implement ETL/ELT, MLOps, model tracking with MLflow, performance tuning, monitoring, and data governance across cloud storage platforms. Collaborate with architects, data scientists, and clients to deliver scalable, secure AI/ML solutions.