Staff AI Engineer

The Staff AI Engineer role sits within Nova Biomedical’s Data & AI function as a senior, hands-on technical leader. The position focuses on designing, building, and operationalizing AI-enabled applications and platform capabilities that can be adopted securely at enterprise scale.

Responsibilities

• Lead the design, build, deployment, and support of AI agents, copilots, RAG applications, tool-calling workflows, and AI-enabled workflow automations.
• Define and implement reusable AI engineering patterns across prompt design, retrieval strategies, embeddings, vector stores, orchestration, API integration, evaluation, guardrails, logging, monitoring, and incident response.
• Build and evolve shared AI platform components including agent orchestration services, model gateway patterns, retrieval services, prompt and policy management, evaluation frameworks, secrets handling, deployment templates, and reusable integration libraries.
• Design platform controls for model access, environment separation, telemetry, cost management, usage tracking, audit logging, and responsible AI guardrails to support safe reuse across teams.
• Partner with enterprise architecture, security, data engineering, analytics, IT operations, quality, and business stakeholders to confirm solutions meet enterprise requirements before scaling.
• Convert prototypes into production-ready services with documented architecture, deployment steps, ownership model, monitoring approach, and support procedures.
• Establish evaluation harnesses that test factuality, retrieval quality, safety behaviors, workflow completion, and regression risk across releases.
• Contribute to Nova’s AI control plane and agent ecosystem by building repeatable integration patterns with enterprise systems, data platforms, business applications, and automation tools.
• Provide technical leadership to internal engineers and implementation partners, including code review, design review, reusable templates, and implementation playbooks.
• Balance speed and governance by delivering business-useful solutions while maintaining security, privacy, auditability, and maintainability.

Requirements

• 10+ years hands-on experience building AI, machine learning, automation, data, or modern software applications in enterprise environments.
• Strong proficiency in Python or similar programming languages, with experience in APIs, cloud services, application integration, CI/CD concepts, and version control.
• Deep familiarity with LLMs, RAG patterns, embeddings, vector databases, prompt engineering, agentic workflows, workflow orchestration, and model or application evaluation.
• Experience designing or operating AI platform capabilities such as model gateways, agent platforms, shared retrieval services, prompt registries, evaluation pipelines, observability stacks, or reusable deployment frameworks.
• Ability to make sound architecture decisions across build versus buy, prototype versus production, and centralized platform versus use-case-specific implementation.
• Experience documenting technical decisions, tradeoffs, runbooks, operating procedures, and support models so internal teams can maintain them.
• Strong collaboration and communication skills across business, technical, security, data, and quality stakeholders.
• Ability to mentor other contributors while staying hands-on with code, integration, testing, and troubleshooting.

Technologies

• Python, LLMs, RAG, embeddings, vector databases, vector stores
• Agent orchestration services, model gateways, API integration, CI/CD, cloud services
• Azure OpenAI, Microsoft Azure, Power Platform, Microsoft Fabric, Databricks, AWS AI services
• Microsoft 365, Salesforce, SAP, ServiceNow
• MLOps, LLMOps, infrastructure-as-code, containerized services, identity integration
• Secrets management, data governance, identity and access management, privacy, responsible AI

Location and Salary

Location: Waltham, MA (hybrid)

Compensation: USD 230,000 - 280,000 per yearly

Experience: 10+ years

Benefits

Employment terms, work location, schedule, compensation, and benefits will align with Nova’s standard HR practices for the final approved position and posting location.

Why This Role Matters

• Shape the engineering foundation for Nova’s enterprise AI capability, including agents, retrieval, evaluation, integrations, and production support patterns.
• Translate high-priority business needs into practical AI applications with measurable adoption and enterprise impact.
• Help establish standards that make AI solutions secure, auditable, maintainable, and reusable across Nova.
• Mentor engineers and delivery partners while maintaining hands-on involvement for critical use cases.

Preferred Experience

• Experience with Microsoft Azure, Azure OpenAI, Power Platform, Microsoft Fabric, Databricks, AWS AI services, or modern agent frameworks.
• Experience applying AI in regulated, GxP, medical device, diagnostics, healthcare, manufacturing, or other data-sensitive business environments.
• Experience with evaluation harnesses, prompt security, model monitoring, agent observability, prompt or policy versioning, and audit-ready AI delivery practices.
• Experience with MLOps, LLMOps, platform engineering, infrastructure-as-code, containerized services, service deployment, identity integration, secrets management, and production observability.
• Experience integrating AI applications with enterprise systems such as Microsoft 365, Salesforce, SAP, ServiceNow, document repositories, data warehouses, APIs, or workflow automation platforms.
• Familiarity with data governance, information security, identity and access management, privacy, retention, and responsible AI practices.

What Success Looks Like

• Priority AI use cases are delivered as working, documented applications or agents adopted by target users.
• AI solutions are secure, maintainable, monitored, evaluated, and ready for internal ownership after launch.
• Reusable code, templates, reference architectures, and implementation playbooks accelerate future AI delivery.
• The AI platform provides reusable services, standards, and controls that reduce one-off development and make secure AI delivery faster for future use cases.
• Internal engineers and implementation partners follow consistent engineering patterns for AI development, deployment, evaluation, and support.
• Business stakeholders experience Data & AI as a strategic partner delivering outcomes, not only as a platform provider.