AI Engineer

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause

Senior Software Engineer – Applied AI

• Lead advanced development efforts and collaborate closely with pipeline engineering and deployment teams
• Architect solutions, tackle sophisticated challenges across Sea-based systems and deliver capabilities that exceed current performance benchmarks through the integration of emerging AI technologies, advanced features, and next-generation methodologies
• Strong collaboration with team’s in the design and implementation of AI architectures to include operational status, coding improvements, dashboard tooling including security
• Supports Leidos AI initiatives, providing insight into technologies, tooling and practices
• Designs, secures and automates AWS cloud initiatives while optimizing agent and customer experiences to full AI potential
• Optimize and enhance customer experiences, enhancing software development flows, quality metrics, KPIs, and Dashboards with AI
• Provides broad-based experience in the systems engineering lifecycle and applies experience/knowledge to cloud initiatives relating to architectural design and development
• Leads team’s to deploy and configure services with AI tools
• Ensures AI models, pipelines, and cloud-deployed services adhere to governance, compliance, and security standards, providing guidance on responsible AI implementation across mission systems

Senior AI Software Engineer

• 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

• Lead AI solutions and development
• Collaborate with pipeline development and deployment teams
• Optimize and enhance customer experiences with AI
• Deploy and configure services with AI tools
• Design and secure AWS cloud initiatives
• Provide broad-based experience in the systems engineering lifecycle

AI Software Engineer – Lead

• Architect and lead the development of agentic AI systems that automate finance workflows.
• Design and implement multi-agent systems leveraging LLMs and orchestration patterns.
• Translate cutting-edge research into scalable solutions.
• Establish guardrails and responsible AI practices for safe outputs.
• Lead the design of advanced predictive models for customer behavior forecasting.
• Develop interpretable, production-grade models for retention strategies.
• Design scalable AI/ML systems with emphasis on software engineering best practices.
• Establish MLOps best practices for AI systems deployment and governance.
• Set technical direction for AI/ML initiatives across finance.

Principal Applied AI Engineer, Finance

• 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 and implement agentic AI systems capable of multi‑step reasoning, tool use, and autonomous task execution.
• Architect enterprise‑grade GenAI solutions, including RAG pipelines, embedding strategies, orchestration layers, and evaluation frameworks.
• Lead PoCs and pilots and scale successful solutions into production‑ready capabilities.
• Establish reusable architectural patterns, reference implementations, and technical standards for GenAI and agentic systems.
• Integrate AI solutions with existing platforms, data sources, and workflows to support adoption.
• Mentor team developers and contribute to raising overall GenAI and agentic AI maturity.
• Embed governance, security, evaluation, and monitoring considerations into solution design and deployment.

PharmSci Agentic and Generative AI Architect

• 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

Director of Forward Deployed AI Engineering

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