AI Engineer

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

LLM Engineer

• Design and maintain a safety evaluation framework—adversarial prompt sets, scenario-based test suites, and regression suites—so that every model and agent update is validated before it ships.
• Lead structured red-teaming exercises covering jailbreaks, prompt injection, tool misuse, and data exfiltration; document findings and drive each issue through to remediation and closure.
• Build and iterate on guardrail logic, including input/output filtering, tool-boundary constraints, action validation, sensitive-data redaction, and policy prompting.
• Integrate safety checks into CI/CD and runtime so that unsafe behavior is intercepted before it reaches users.
• Perform threat modeling for agentic scenarios: tool-call boundaries, sandbox isolation, and least-privilege access, with particular attention to preventing agents from exfiltrating data or executing irreversible actions through chained tool calls.
• Conduct safety reviews of reinforcement-learning (RL) environments and trajectory data, partnering with environment and agent engineering teams to embed safety constraints directly into the environments themselves.
• Instrument AI features for safety with structured logging, tracing, and metrics, enabling detection of unsafe patterns and regressions in production.
• Prepare evidence for governance reviews—test reports, evaluation summaries, and mitigation validation—aligned with internal Responsible AI standards.
• Collaborate with Product and UX to improve safety interactions (warnings, confirmations, refusal messaging, and feedback collection), and align evaluation goals with the Research and Data teams.

AI/LLM Safety Engineer

• Design, build, and deploy core application components of EVERSANA’s AI Agency Platform.
• Develop backend services, APIs, and front-end interfaces integrated with third-party systems.
• Collaborate with AI/ML Engineers to integrate model endpoints and services.
• Maintain code quality, testing coverage, and deployment reliability across environments.
• Ensure platform design meets pharma regulatory expectations for traceability and content integrity.

Senior AI Developer, Engineering

• Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
• Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
• Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
• Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
• Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
• Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
• Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
• Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
• Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.
• Support enterprise AI governance, compliance, auditability, and model risk management requirements.
• Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
• Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
• Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
• Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
• Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
• Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
• Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
• Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
• Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
• Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
• Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
• Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
• Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Senior MLOps, Generative AI Engineer

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

Machine Learning Engineer

• Design, build, and operate scalable ML and data pipelines for spatial-temporal and sensor-driven datasets.
• Operationalize data science algorithms into reliable, distributed ML workflows covering feature extraction, training, evaluation, inference, and model lifecycle management.
• Implement and maintain containerized ML workloads in cloud-native environments.
• Integrate model outputs into downstream serving systems and analytical platforms to support web-based applications and operational decision-making.
• Develop and maintain CI/CD pipelines for ML and data services.
• Collaborate closely with data scientists to operationalize experimental models into reproducible, observable, and scalable production systems.
• Take ownership of MLOps practices within an applied research team, bringing structure, repeatability, and best practices to evolving environments.

MLOps Engineer

• Lead and scale the Forward Deployed AI Engineering team across its two functions: customer-embedded engineering and AI & Data Science
• Own the team's mandate: embed in strategic customer accounts to discover problems, build high-grade solutions, and produce cross-customer intelligence that elevates the entire platform
• Define the engagement model for FDE work — how engineers are assigned to accounts, how engagements are scoped, and how solutions are documented and productized
• Oversee the AI & Data Science function — ensuring cross-customer telemetry, threat intelligence, and detection optimization work is translated into tools that the SE&A team can operate
• Partner with Sales to identify strategic accounts that are candidates for FDE engagement
• Partner with the SE&A and Rapid Deployment teams to ensure FDE-built solutions flow back into the broader delivery model
• Serve as an executive-level presence with strategic customers — participating in account reviews and helping define the roadmap for embedded engagements
• Evaluate new AI technologies, data science tooling, and engineering approaches that can improve team output and customer outcomes
• Hire, develop, and retain engineers across both the FDE and AI & DS functions

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