Lead AI Engineer, Stealth Longevity AI Startup

Lead AI Engineer

Stage: Early-Stage, Pre-Series A Healthcare Technology Company
Reports to: CTO, with close collaboration with the COO
Location: Remote, with occasional travel as needed

About the Company

Bedrock Talent is partnering with an early-stage healthcare technology company building an AI-first platform designed to generate clinically meaningful, explainable insights from complex healthcare data. The company is developing advanced AI and analytics capabilities across longitudinal datasets to help healthcare organizations make more informed decisions and improve outcomes.

Because the company is conducting this search confidentially, additional information will be shared with qualified candidates during the interview process.

Position Overview

The Lead AI Engineer will own end-to-end engineering execution, AI/ML system development, and technical leadership during a pivotal stage of the company’s growth.

This person will lead the design and delivery of the company’s AI-first platform, including agent-based architectures, data pipelines, model orchestration, and production systems supporting advanced cohort analysis and longitudinal insight generation.

This is a hands-on builder and technical leadership role. The Lead AI Engineer will translate product requirements, data science models, and scientific frameworks into scalable, secure, and production-ready systems.

The role works closely with Data Science, Product, Operations, Medical, and Research leaders to ensure the platform delivers clinically meaningful, explainable, and high-performing AI-driven insights.

Core Responsibilities

Platform and Agent-Based Architecture

• Lead the design and implementation of the company’s AI-first platform architecture, including:

  • Agent-based systems, such as matching, reasoning, and orchestration agents

  • Model orchestration layers and inference pipelines

  • Data ingestion, transformation, and feature pipelines

• Build and operationalize systems supporting:

  • Proprietary analytical framework execution

  • Cohort matching and comparative analysis

  • Longitudinal and temporal data analysis

• Ensure systems are modular, extensible, and designed to support future scale

Engineering Execution and Delivery

• Own engineering execution and delivery across product initiatives and company milestones, including work completed with external resources and partners

• Translate product requirements, PRDs, and user stories into clear technical plans and deliverables

• Drive sprint planning, execution, and delivery in close partnership with Product

• Establish a predictable release cadence while balancing speed, quality, and technical debt

• Quickly understand, improve, and take ownership of existing production systems

Technical Leadership and Architecture

• Serve as the company’s senior hands-on engineering leader in partnership with the CTO

• Make pragmatic architecture and technology decisions appropriate for an early-stage environment

• Design systems for future scalability, security, and compliance without over-engineering

• Establish technical standards, documentation, and engineering best practices

• Help lead and mentor a small engineering team as the company grows

AI/ML Productionization and MLOps

• Partner closely with Data Science leadership to productionize models and analytical frameworks

• Implement MLOps and LLMOps practices, including:

  • Evaluation harnesses

  • Prompt and version management

  • Retrieval-quality monitoring

  • Output grounding and citation integrity

  • Latency and token-cost monitoring

• Ensure models and AI systems are:

  • Scalable and performant

  • Reproducible and testable

  • Observable and auditable

  • Reliable in production environments

Quality Assurance and Reliability

• Own engineering quality and QA execution throughout the development lifecycle

• Establish testing practices for:

  • AI and machine-learning systems

  • Data pipelines

  • APIs and integrations

  • Full-stack product functionality

• Implement CI/CD pipelines, automated testing, and release-readiness gates

• Ensure system reliability, performance, and observability

Data Infrastructure, Compliance, and Integrations

• Design and implement infrastructure for integrating with EHR data, claims data, longitudinal healthcare datasets, and relevant research or genomic data sources

• Partner with Data Science and Product to ensure engineering practices support healthcare privacy and security expectations

• Ensure the platform supports HIPAA-aligned data handling and security practices

• Collaborate with Operations leadership on compliance-related technical requirements

• Build secure, scalable integrations with external systems and data partners

Cross-Functional Collaboration

• Work closely with the CTO and COO to align engineering execution with company priorities

• Partner closely with Product on requirements, scope tradeoffs, and delivery timelines

• Collaborate with Data Science, Medical, and Research leaders to translate scientific direction into technical implementation

• Support customer implementation and service teams with technical onboarding, integrations, and issue resolution

• Communicate technical decisions, risks, and tradeoffs clearly to both technical and nontechnical stakeholders

Release Management and Incident Response

• Own release planning, deployment readiness, and rollback strategies

• Monitor production performance, uptime, and system health

• Lead incident response and postmortem processes

• Drive continuous improvements in reliability, observability, and operational readiness

Operations and Execution Discipline

• Contribute to lightweight operating rhythms, including planning sessions, execution reviews, and retrospectives

• Surface technical and execution risks early and recommend practical solutions

• Partner with Operations leadership on resourcing, sequencing, and delivery tradeoffs

• Create enough structure to support reliable execution without slowing down an early-stage team

Qualifications and Experience

Required

• 6+ years of software engineering experience, including full-stack web application development, relational database design, event-driven systems, and cloud-based production environments

• Recent, meaningful experience using AI coding tools and agents, such as Claude Code or comparable platforms

• Bachelor’s or master’s degree in computer science, or equivalent practical experience

• Experience working on highly collaborative, small engineering teams within high-growth startup environments

• Interest in helping lead and mentor a small team while remaining deeply hands-on

• Experience building and productionizing LLM, generative AI, or other AI-driven applications

• Experience in healthcare technology, digital health, or another regulated environment

• Demonstrated success delivering production SaaS or platform-based products

• Strong knowledge of modern software development practices, CI/CD, microservices, cloud infrastructure, and production operations

• Ability to quickly understand and take ownership of existing production systems

• Proven ability to operate effectively amid ambiguity, rapid change, and evolving priorities

• Exceptional communication skills and genuine enthusiasm for working in a mission-driven environment

Preferred

• Enjoys collaborating closely with Product and Design

• Experience working with Model Context Protocol tools or MCP-based environments

• Familiarity with healthcare security, compliance, and audit requirements

• Experience partnering with Product, Data Science, Medical, or Research teams

• Experience helping a startup scale through the Series A or Series B stage

• Experience with healthcare data sources, including claims, EHR, clinical, genomic, or longitudinal datasets

Success in the First 12–24 Months

• MVP and early product milestones are delivered on time

• The company has a stable and reliable platform supporting early customers

• QA, testing, and release discipline are embedded throughout the engineering lifecycle

• Product, Data Science, Operations, and Engineering work together effectively

• AI and analytical systems are explainable, testable, observable, and production-ready

• The company has strong technical and operational foundations in place for Series A growth

Compensation and Equity

• Competitive base salary commensurate with experience

• Meaningful equity participation aligned with the company’s stage and the role’s impact

• Benefits package including healthcare coverage, paid time off, and professional development support