• Manage and evolve the end-to-end architectural blueprint for cloud-native applications at scale — covering compute, data, networking, security, and observability layers on AWS. • Define and promote agentic AI architecture patterns, including multi-agent systems, tool orchestration, memory and retrieval strategies, LLM routing, guardrails, and feedback loops. • Translate these patterns into relevant standards and reusable reference architectures. • Establish and govern DevOps standards across the organization: CI/CD pipeline design, GitOps practices, deployment strategies (blue/green, canary, progressive delivery), environment parity, and release engineering. • Design and oversee platform infrastructure. This includes ECS, Kubernetes clusters (EKS), service mesh, API gateway strategy, secrets management, IAM/RBAC governance, and data stores like DynamoDB, Aurora RDS, Postgres, Elastic/OpenSearch. Additionally, it involves multi-account/multi-region AWS topology. • Lead architecture reviews and provide binding technical decisions on high-risk changes — balancing velocity, reliability, cost, and security. • Define SLOs/SLIs/error budgets, logging standards, distributed tracing, capacity planning, and incident response frameworks. • Introduce new technologies through structured PoC and risk assessment processes; build internal communities of practice around architectural patterns. • Produce architecture decision records (ADRs), system context diagrams, threat models, and runbooks that serve as the source of truth for platform design. • Be a technical advisor to Product and Partners — translating architectural constraints and capabilities into clear strategic options. • Mentor staff and senior engineers on architectural thinking, systems design, and engineering leadership; conduct design reviews that raise the technical bar across teams.

• You will architect and implement the core AI pipeline that powers Accel's test creation system. • Work closely with the founder to design and build an AI-powered content generation system from the ground up. • You'll contribute to meaningful parts of the product end-to-end from how the system ingests and understands source material, to how it produces and validates outputs, to how instructors interact with and review what the system generates. • On the engineering side, you'll build and iterate on LLM-driven pipelines, work with retrieval and embedding techniques to ground outputs in real source material and develop backend services and APIs that tie everything together. • Beyond pure coding, you'll be expected to think about output quality and building evaluation steps, catching failure modes, and improving the system based on real instructor feedback. • You'll research new tools and techniques as the AI space evolves and bring relevant ideas directly into the product. • This is a generalist role at an early-stage product where you'll wear multiple hats, work with ambiguity, and have direct input into how things are built.

• Pipeline Development: Build and execute outbound sequences targeting senior decision-makers. • Qualification: Run discovery calls to separate real opportunities from the noise. • Strategy: Work with the founders to refine our ICP and messaging based on real-world feedback. • Stack Optimization: Leverage modern tooling (Clay, Sales Nav, etc.) to automate the manual parts of prospecting so you can focus on the strategy.

• Responsible for researching, designing, developing, and implementing Artificial Intelligence (AI) solutions • Work with identity architects, cybersecurity engineers, data analysts, software developers, and mission stakeholders • Identify opportunities where AI can improve identity analytics, access governance, operational efficiency, cybersecurity, and decision support • Help establish and mature AI capabilities across the ICAM ecosystem by leveraging machine learning, generative AI, predictive analytics, natural language processing, and intelligent automation technologies • Support the full lifecycle of AI initiatives from concept development and experimentation through production implementation and sustainment • Ensure compliance with DoW security requirements and responsible AI principles • Design, develop, test, and implement AI and machine learning solutions supporting enterprise ICAM objectives • Identify opportunities to improve identity governance, access management, onboarding processes, and operational workflows through AI-driven capabilities • Develop predictive analytics, anomaly detection, behavioral analysis, and risk-scoring models using identity and access management data • Collaborate with engineers and architects to integrate AI capabilities into existing ICAM platforms and enterprise services • Evaluate emerging AI technologies and recommend solutions applicable to ICAM, Zero Trust, and cybersecurity use cases • Develop proof-of-concepts, prototypes, and pilot programs to validate AI capabilities before enterprise adoption • Provide technical leadership and subject matter expertise regarding AI technologies and industry best practices.