Staff AI Engineer

Career Opportunities with Nova Biomedical Corporation

Nova Biomedical is an Equal Opportunity Employer in compliance with affirmative action in hiring and promoting women, minorities, veterans and individuals with disabilities.

Location:

Hybrid / Remote aligned to Nova business needs

Salary Range:

$230,000 to $280,000 yearly

Company Overview

Nova Biomedical: One Global Brand. One Vision. Together under one name. Advanced Instruments and Nova Biomedical are now united under one brand, Nova Biomedical. This role will help scale Nova's Data & AI capability as the company strengthens its digital foundation, analytics, and AI-enabled ways of working.

Driven by innovation and guided by our core values - Customer Centricity, Ingenuity, Ownership & Accountability, Collaboration, and Integrity - Nova creates solutions that make a global impact. At Nova, candidates will find opportunities to grow, contribute, and help build a culture focused on transparency, sustainability, effective communication, and employee engagement.

Job Summary

The Staff AI Engineer will serve as a senior hands‑on technical leader within Nova's Data & AI function, designing, building, and operationalizing AI-enabled applications, agents, reusable engineering patterns, and the platform capabilities needed to run them at enterprise scale. This role combines deep AI engineering capability with AI platform engineering discipline, helping move priority use cases from concept to secure, maintainable production adoption.

The role is expected to set technical direction for AI engineering and AI platform patterns, partner closely with data, analytics, IT, security, quality, and business teams, and ensure that AI solutions are built with strong architecture, evaluation, observability, identity and access controls, documentation, and handoff practices from the start.

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 remaining close to hands‑on implementation for critical use cases.

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 such as 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 so AI capabilities can be reused safely across teams.

Partner with enterprise architecture, security, data engineering, analytics, IT operations, quality, and business stakeholders to ensure AI solutions meet enterprise requirements before scale.

Convert prototypes into production‑ready services with documented architecture, deployment steps, ownership model, monitoring approach, and support procedures.

Establish practical 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 building solutions that are useful for the business while preserving security, privacy, auditability, and maintainability.

Qualifications

10+ years 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 in a way that can be maintained by internal teams.

Strong collaboration and communication skills, with the ability to work across business, technical, security, data, and quality stakeholders.

Ability to mentor other technical contributors while remaining hands‑on with code, integration, testing, and troubleshooting.

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 is strongly preferred.

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 that are 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 that helps deliver outcomes, not only as a platform provider.

Benefits and Growth

This is a staff‑level role designed for high‑impact technical contribution and leadership within Nova's growing Data & AI function. The role offers the opportunity to build foundational AI engineering and AI platform capabilities, influence enterprise AI architecture, create reusable services and operating patterns, mentor technical contributors, and help shape how Nova applies AI responsibly across the business.

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

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