Staff AI Engineer

Nova Biomedical•Waltham, MA

About The Position

The Staff AI Engineer is a senior, hands-on technical leader within Nova’s Data & AI function. The role designs, builds, and operationalizes AI-enabled applications, agents, reusable engineering patterns, and platform capabilities for enterprise-scale use. It combines AI engineering with AI platform engineering to move priority use cases from concept to secure, maintainable production adoption. The engineer sets technical direction for AI engineering and platform patterns and partners with data, analytics, IT, security, quality, and business teams. Solutions are expected to incorporate sound architecture, evaluation, observability, identity and access controls, documentation, and handoff practices from the outset. The role also provides technical leadership and mentorship while staying directly involved in implementation. The position helps establish Nova’s enterprise AI engineering foundation, deliver practical AI applications with measurable adoption and business impact, and create standards for secure, auditable, maintainable, and reusable AI solutions. Work location is described as hybrid or remote, aligned to Nova business needs. The listed salary range is $230,000 to $280,000 yearly.

Requirements

At least 10 years of hands-on experience building AI, machine learning, automation, data, or modern software applications in enterprise environments.
Strong proficiency with Python or similar programming languages, 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 choices 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 for maintenance by internal teams.
Strong collaboration and communication skills across business, technical, security, data, and quality stakeholders.
Ability to mentor technical contributors while remaining hands-on with code, integration, testing, and troubleshooting.

Nice To Haves

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.

Responsibilities

Lead the design, development, deployment, and support of AI agents, copilots, RAG applications, tool-calling workflows, and AI-enabled workflow automations.
Define and implement reusable AI engineering patterns for prompt design, retrieval, 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.
Partner with enterprise architecture, security, data engineering, analytics, IT operations, quality, and business stakeholders to ensure solutions meet enterprise requirements before scaling.
Turn prototypes into production-ready services with documented architecture, deployment steps, ownership models, monitoring approaches, and support procedures.
Establish evaluation harnesses for factuality, retrieval quality, safety behaviors, workflow completion, and regression risk across releases.
Build repeatable integration patterns with enterprise systems, data platforms, business applications, and automation tools as part of Nova’s AI control plane and agent ecosystem.
Provide technical leadership to internal engineers and implementation partners through code and design reviews, reusable templates, and implementation playbooks.
Balance delivery speed and governance while preserving security, privacy, auditability, and maintainability.
Mentor technical contributors while remaining hands-on with code, integration, testing, and troubleshooting.