Staff AI Engineer

Nova Biomedical; Waltham (MA); Hybrid; USD 230,000 - 280,000; Full time

Job summary

Nova Biomedical seeks a Staff AI Engineer to lead the design, build, and operation of enterprise‑scale AI-enabled applications and platform capabilities within the Data & AI function. You will mentor engineers, define reusable patterns, and ensure secure, auditable delivery across teams.

You will collaborate with architecture, security, data, and ops teams to turn prototypes into production services, with strong emphasis on governance, observability, and measurable enterprise impact.

Qualifications

• 10+ years of hands-on experience building AI, ML, automation, data, or modern software applications in enterprise environments.
• Strong Python skills with API, cloud services, CI/CD concepts, and version control.
• Deep familiarity with LLMs, RAG patterns, embeddings, vector databases, prompt engineering, agentic workflows, and evaluation.

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.

Skills

Python

LLMs

RAG patterns

Embeddings

Vector databases

Prompt engineering

Agent workflows

Workflow orchestration

Model evaluation

Mentorship

Tools

Azure OpenAI

Microsoft Azure

Power Platform

Microsoft Fabric

Databricks

AWS AI services

Microsoft 365

Salesforce

SAP

ServiceNow

Job description

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.