Marvik

• Partner with the CTO and leadership to set the Intelligence strategy and roadmap; own the execution. • Build, hire, and develop the Intelligence team — set the bar for craft, shape the operating cadence, and build the collaboration patterns with product, platform, and engineering. • Stand up the canonical data substrate: schema discipline, tenancy isolation, data contracts, lineage, and governance that AI/ML workloads run cleanly against. • Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, and MLOps practice. • Lead the learning and reasoning capabilities of the platform: RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence. • Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness — make AI workloads observable to engineering, product, and customer success. • Drive the build-vs-buy posture for the AI/ML stack; set production readiness standards for AI workloads in close collaboration with the platform team. • Partner with product on the AI use case portfolio; engage directly with customers when needed to ground Intelligence decisions in real workflow problems.

• Build and operate ingestion, ELT/ETL, and orchestration pipelines that move data from our MongoDB Atlas operational store and other sources into our analytical and AI-serving layers • Implement layered (medallion-style) transformations with idempotent, backfillable, incrementally loaded jobs • Apply deduplication, normalization, and validation so downstream data is high-quality and trustworthy • Modernize legacy / homegrown data flows via incremental, strangler-fig migrations that keep production stable • Build embeddings and vector pipelines, and the feature/retrieval-ready datasets that RAG, semantic search, and agentic workloads depend on • Make production data AI-ready in practice: well-structured, lineage-tracked, and retrieval-friendly, in partnership with ML and application engineering • Implement real-time and change-data-capture flows from MongoDB (Change Streams / CDC) where workloads require fresh data • Implement the canonical data model, schemas, and data contracts defined by the Data Architect — enforced in-repo so other teams build against stable definitions • Exercise sound persistence judgment in execution: land data in the right store (document / NoSQL, vector, analytical) per the architectural direction • Contribute to build-vs-buy decisions by prototyping with proven, industry-standard tooling over custom development • Establish testing, data-quality, and lineage checks for the pipelines you own, with clear alerting and runbooks • Instrument pipeline observability (freshness, volume, schema-drift, cost) so failures are caught before consumers feel them • Use AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier for transformation logic, query tuning, and migration scripting • Partner with database engineering on extracting from and protecting the production store • Partner with the Data Architect on implementing target-state patterns and surfacing what's hard to build • Partner with ML, AI, and application engineers on the data they consume — shaping and governing it so it's safe and ready to build on

• Delivery Ownership: Take end-to-end ownership of project outcomes (scope, timelines, quality, and risks) for high-profile global clients. • Execution Excellence: Remove non-technical blockers, adapt staffing, and ensure agreed delivery processes are followed and optimized. • Technical Judgment: Evaluate trade-offs and challenge assumptions to guide decisions, acting as a bridge between technical teams and business value. • Stakeholder Management: Build strong operational trust with enterprise clients, managing expectations transparently regarding risks and changing priorities. • Team Leadership: Build and develop high-performing teams, ensuring workload balance, engagement, and alignment. • Delivery-Led Growth: Identify opportunities for renewals and expansions based on the value delivered and client trust.

• Lead discovery sessions with SMEs and stakeholders to understand, challenge, and map complex business processes at both high-level and detailed operational levels • Define end-to-end decision logic, workflow behavior, exception handling, and validation criteria in close collaboration with client stakeholders, operations teams, and engineering • Actively define — together with engineers and stakeholders — the scope, boundaries, decision points, and expected behaviors of AI agents, copilots, and workflow automation solutions across claims, benefits, care management, and related domains • Structure and formalize business rules, operational criteria, and system behaviors from policies, SOPs, legacy systems, and stakeholder input into clear functional logic that technical teams can implement • Drive validation and testing efforts: define acceptance criteria, test scenarios, and evaluation approaches to ensure alignment with expected outcomes • Work directly with client stakeholders and cross-functional internal teams to refine requirements, resolve ambiguities, surface tradeoffs, and support prioritization throughout delivery • Maintain clear documentation of workflows, decision trees, business rules, system interactions, and expected outputs, ensuring shared understanding across all teams

• Set architectural direction for products emerging from Labs, from prototype to production • Design and build production-grade agentic workflows: retrieval, tool use, multi-step planning, human-in-the-loop checkpoints, and safety guardrails • Be the senior technical voice in architectural decisions that span multiple teams • Establish technical standards and engineering practices that extend across the organization • Make significant individual contributions to the SaaS product across Azure and AWS • Collaborate with product, design, and engineering across the full SDLC • Mentor and guide engineers and actively influence engineering culture • Ensure performance, quality, and responsiveness of customer-facing applications and the agentic systems built on top of them • Implement and maintain modern automated testing practices, including evals for AI components • Contribute to modernization and consolidation of legacy surfaces as integration touch points emerge

• Willing to take ownership of machine learning projects that include state-of-the-art solutions • Participating in the research and design of new solutions and complex architectures • Being proactive in proposing new tools, solutions, and methodologies • Working with object trackers deployed in edge hardware, using the latest NLP models, building generative models for image and video, work with the latest libraries and frameworks (LangChain, Diffusers, TensorRT, etc)

• Own the architectural vision and tech roadmap spanning all Stratus product lines • Drive AI leverage as the spine of value delivery • Lead the decomposition of the ASP.NET monolith into a modular service architecture • Set engineering standards through working reference implementations, POCs, in-repo templates, and guardrails • Partner with the Data Architect to define cross-product data architecture and integration strategy • Drive reliability, performance, and operational excellence with the platform team • Mentor senior engineers and raise the technical bar across teams • Engage directly with customer-facing teams and customers to ground architectural decisions in real workflow problems

• Work hand-in-hand with leadership and other teammates to prioritize and execute on our product roadmap • Design, develop and own complex features or software components • Deliver robust, scalable, and well-tested code • Help develop and establish best practices to lay the foundation for a high-performing technology team and its culture • Collaborate with Design and Operation teams to keep making Aleph better every day.

• Work hand-in-hand with leadership and cross-functional teammates to prioritize and execute on our product roadmap. • Own the end-to-end development of new AI features, from rapid prototyping to full implementation in our core product. • Deliver robust, scalable, and thoroughly tested code. • Help define and establish best practices that lay the foundation for a high-performing engineering team and culture. • Collaborate with Design and Operations teams to make Aleph better every day.

• Lead the design, development, and deployment of enterprise AI/ML solutions • Collaborate with business and engineering teams to define and prioritize AI projects • Drive rapid proof-of-concept development for AI initiatives • Mentor your team and foster continuous learning • Take ownership of ML projects with state-of-the-art solutions • Research and design new solutions and complex architectures • Proactively propose new tools, solutions, and methodologies • Ensure project scalability and robustness • Manage cross-team communication and planning • Participate in team hiring