Eliza

DevOps Engineer

• Engage business leaders to understand priorities and identify valuable AI applications
• Translate business problems into credible AI concepts and working technical experiences
• Help qualify opportunities with a clear view of fit and value
• Build industry-specific plugins, prototypes, demonstrations, and ChatGPT experiences
• Demonstrate what organizations can achieve with AI
• Lead AI enablement, AI-SDLC, and art-of-the-possible workshops
• Partner with OpenAI teams to prepare for customer meetings, events, and one-to-many programs
• Turn successful ideas into reusable workshop assets, technical patterns, and playbooks
• Transition qualified delivery work to Eliza's AI Deployment Managers and Forward Deployed Engineers

Partner AI Deployment Engineer

Full-stack Engineer

• Own end-to-end delivery quality for a major engagement, workstream, or solution area.
• Translate ambiguous client needs into practical execution plans, technical milestones, and shipped outcomes.
• Maintain a clear view of status, risks, blockers, and dependencies within your scope.
• Raise quality concerns early and correct course before client confidence is affected.
• Lead solution architecture and technical decision-making for your workstream.
• Make pragmatic tradeoffs between speed, quality, scope, maintainability, cost, latency, and client value—and explain your reasoning.
• Review critical technical work before it is shared with clients or treated as production-ready.
• Design and ship AI/ML solutions using Python, modern ML frameworks, LLM orchestration tooling, and cloud-native infrastructure.
• Act as the senior technical voice on your engagement, credible with both engineers and executives.
• Spot technical, delivery, scope, timeline, and client-alignment risks early.

Senior Forward Deployed Engineer

• Own Portfolio Delivery Quality: Maintain a clear view of delivery status, risks, blockers, and priorities across assigned accounts
• Set Technical Direction Across Engagements: Guide solution architecture and technical decision-making across multiple concurrent engagements
• Coach and Develop the Field Team: Provide direct, timely, actionable feedback on technical execution, client communication, ownership, and judgment
• Maintain Client Confidence: Serve as the senior technical and delivery presence for clients across your portfolio
• Establish Execution Discipline: Define the right operating cadence for each account
• Manage Delivery Risk and Escalation: Identify technical, staffing, scope, timeline, and client-alignment risks across the portfolio

Lead Forward Deployed Engineer

Engineer

• Build and test components of custom AI systems—model integrations, data pipelines, APIs, and deployment tooling—under senior technical direction.
• Participate in client sessions across the engagement lifecycle: requirements gathering, prototype reviews, technical working sessions, and delivery readouts.
• Take direct ownership of scoped client-facing assignments, with responsibility growing over your first several months.
• Prototype rapidly and iterate against real client feedback.
• Proactively identify opportunities to apply AI within Eliza's own operating rhythms.
• Scope, build, and present high-impact internal solutions end-to-end.
• Write clean, reviewable code and respond productively to technical review.
• Learn and apply Eliza's delivery standards for quality, documentation, and pace.
• Keep the engagement team informed on status without needing to be chased.
• Work closely with Lead and Senior FDEs, absorbing different approaches to technical and client problems.

Associate Forward Deployed Engineer

Artificial Intelligence

• Design and build Eliza's new healthcare practice from the ground up — while continuously improving how Eliza delivers, working with cross-functional teams of AI Product Managers and Forward Deployed Engineers.
• Shape and sell solutions with senior healthcare buyers: partnering with CFOs, revenue-cycle, and clinical leaders to define the problem, scope the work, and win the engagement — not just advise once it's sold.
• Make the practical AI delivery calls — what to automate now, what needs deeper integration, and where the clinical, compliance, and financial risk sits — and translate those calls into clear, compelling narratives clients trust.
• Own the full engagement arc from SOW creation to solutions delivery: challenge the status quo, experiment boldly, and bring new thinking to messy, real-world healthcare workflows.
• Manage competing priorities across the new healthcare practice and the broader business, without losing sight of what matters most.

VP Healthcare AI

• Partner directly with client stakeholders to understand requirements, constraints, and business objectives.
• Lead the technical design and hands-on implementation of custom AI systems—including model integration, data pipelines, APIs, and deployment infrastructure.
• Rapidly prototype and iterate with clients in live environments.
• Build and deploy ML/AI solutions using technologies like Python, TensorFlow/PyTorch, LangChain, and cloud-native tools.
• Integrate LLMs and other generative models into client products and workflows.
• Support model fine-tuning, prompt engineering, and evaluation pipelines where applicable.
• Work with internal teams (product, design, research) to shape reusable components and frameworks based on deployment experiences.
• Contribute client feedback and frontline insights to improve service delivery and product strategy.
• Advise client technical teams on best practices for AI/ML development and deployment.
• Deliver hands-on workshops, documentation, and training to enable long-term client success.
• Guide clients through infrastructure and architecture decisions (e.g., cloud, security, scalability).

Forward Deployed Engineer

Product Manager

• Deliver one-to-many trainings and hands-on, persona-specific workshops for client teams, from analysts to executives.
• Build, test, and refine custom skills, prompts, and workflows with client champions; turn what works into repeatable curriculum.
• Conduct persona and use-case interviews with client stakeholders to surface high-value workflows for workshops and skills.
• Track adoption signals throughout the engagement: usage depth, use-case realization, and time savings—and flag what the data says.
• Maintain the scored use case backlog: keep feasibility, data readiness, and priority current as discovery evolves.
• Document requirements from client working sessions into structured inputs the AI PM can shape into specs.
• Execute evals: run test sets, log failure modes, and summarize results so quality calls can be made quickly.
• Run down blockers—access, data, calendars, approvals—so FDEs and the AI PM stay focused on the build.
• Prepare executive readout materials: progress, metrics, risks, and next steps—short, honest, and outcome-oriented.
• Keep engagement artifacts current: roadmaps, status trackers, meeting notes, and action items across concurrent workstreams.
• Contribute to Eliza's reusable delivery assets: training curricula, skill libraries, workshop templates, and playbooks.
• Track new model and product releases relevant to active engagements and surface what matters to the team.

Associate AI Product Manager

• Serve as the technical counterpart to sales throughout the sales process, helping scope what is feasible, what the path to production looks like, and what a realistic engagement structure should be.
• Handle the strategic and feasibility layer of technical conversations with prospects and clients: use case fit, sequencing, data requirements, timeline realism, and risk.
• Ensure that what gets scoped and sold is what can actually be delivered, preventing commitments that do not survive contact with reality.
• Run structured discovery with client stakeholders within active engagements to surface AI use cases, working across business units to understand pain points, workflows, and data landscape.
• Build and maintain a scored use case backlog for each engagement, evaluating opportunities against feasibility, data readiness, and measurable business impact.
• Write clear product specs that translate business problems into technical requirements engineering can build against, covering inputs, outputs, constraints, and success metrics.
• Drive iterative development cycles, working hands-on with prompt engineering and agent design decisions alongside the technical team.
• Own the definition of what success looks like for every AI deployment, connecting model performance to the business outcomes the client actually cares about.
• Serve as the connective tissue between business stakeholders and engineering, ensuring technical teams build what matters and business leaders understand what is possible.