AI Engineer

• lead the technical design, development, and architecture of cutting-edge AI and Generative AI solutions
• serve as a trusted technical advisor to national and international clients
• provide hands-on technical leadership and guidance throughout the entire project lifecycle
• drive thought leadership in the evolving landscape of GenAI
• actively participate in presales activities, providing deep technical expertise for proposal development
• collaborate with clients to understand their business challenges and translate them into actionable AI strategies and technical roadmaps
• lead and manage specific technical workstreams within complex projects
• contribute to the continuous development and technical refinement of Blend360's AI and Generative Solutions offerings

Senior Director, AI Engineering

• Support the development and operationalization of AI and agentic AI systems in production mission environments.
• Collaborate with multidisciplinary teams of analysts, engineers, and developers to solve complex challenges and deliver innovative, production-ready algorithms and platforms.
• Apply cutting-edge techniques in statistical analysis, predictive analytics, entity resolution, graph analytics, explainable AI, and operational analytics.
• Contribute to the integration of AI, DevSecOps, data engineering, platform engineering, cybersecurity, and observability in a fast-paced, engineering-centric environment.

• Build our agentic SDLC pipeline. Design, implement, and operate agentic workflows across the development lifecycle - planning, code generation, review, testing, documentation, and release that is all integrated with our GitOps and CI/CD infrastructure.
• Level up developer experience. Bring modern AI integrations into the daily workflow of our engineering team: coding agents, automated review, intelligent test generation, and self-service tooling that removes friction and multiplies output.
• Engineer grounded, trustworthy systems. Build retrieval-grounded and context-aware AI systems that produce reliable, verifiable results and design the evaluation harnesses that prove it.
• Establish governance and guardrails. Define and implement the policies, human-in-the-loop checkpoints, audit trails, and security controls that let us adopt agentic AI responsibly in environments where trust is non-negotiable.
• Formalize and scale. Turn what works into repeatable frameworks, playbooks, and internal platforms that extend agentic acceleration beyond engineering to the whole organization.
• Champion the practice. Evangelize, teach, and support AI adoption across the team; you'll be a force multiplier, not a silo.

AI Workflow Engineer

• Develop and implement AI/ML models and solutions to support healthcare data initiatives.
• Collaborate with cross-functional teams to ensure successful deployment of AI/ML technologies.
• Analyze and interpret complex healthcare data to drive actionable insights.
• Ensure compliance with healthcare regulations and guidelines regarding data usage.

• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
• Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
• 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, including building the evidence when the cause is a vendor

Senior Software Engineer – Applied AI

• Provide strategic and operational guidance to teams developing AI-enabled solutions.

AI Engineer – SME

• 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

• Design, develop, and deploy large-scale distributed AI applications that power customer self-service across multiple communication channels (voice, IVR, chat)
• Build and optimize multi-agent systems that enable intelligent, collaborative decision-making to improve automation and customer experience
• Collaborate with cross-functional teams (engineering, product, data science) to translate business requirements into scalable AI/ML solutions
• Ensure system reliability, scalability, and performance through best practices in architecture, testing, and monitoring
• Stay at the forefront of AI and distributed systems research, bringing innovative approaches and tools into production environment

Senior Engineer, Interactive Voice Response – AI/ML

• 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

• Contribute to the design of AI-enabled solution architectures within the Avanade Catalyst Solutions portfolio
• Apply and help develop reusable patterns, reference architectures, and accelerators
• Adapt pre-built AI solutions to support client needs and evolving market requirements
• Ensure solutions meet standards for scalability, security, and production readiness
• Collaborate with engineering and delivery teams to identify opportunities for AI-driven tools and methods
• Support the development of repeatable, outcome-based services
• Help drive consistency in how AI solutions are implemented across engagements
• Support architecture and deployment of ready-made AI offerings
• Partner with security, risk, and compliance teams to align with enterprise requirements
• Provide guidance to delivery teams

Manager, Global AI Enterprise Architect

• Lead cross-functional teams through the full product lifecycle
• Conduct market research, competitive analysis, and customer interviews
• Prioritize features and initiatives based on customer feedback
• Drive product development by defining requirements and managing the backlog
• Monitor product performance, analyze metrics, and iterate on features

Senior Product Manager, AI Platform

• Lead the strategic digital and AI partnership with AstraZeneca, serving as the primary executive point of contact and ensuring Evinova’s solutions are embedded in clinical development strategy, portfolio decisions and key programmes.
• Co-create and maintain a multi-year digital and AI roadmap and joint scorecard, driving quarterly governance with AstraZeneca’s most senior R&D leaders.
• Shape and run strategic planning cycles to identify new innovation opportunities, emerging use cases and areas where AI can accelerate study timelines, enhance data quality and strengthen regulatory readiness.
• Act as the executive voice of AstraZeneca within Evinova, translating strategic priorities into clear direction for product, delivery, commercial, science and innovation teams.
• Build and sustain trusted relationships with AstraZeneca’s executive collaborators, including C-suite leaders, VPs in Clinical Development and Operations, Chief Operating Officers and Heads of Regulatory and IT.
• Chair or co-chair senior governance forums, steering committees and executive business reviews to secure alignment, resolve issues and ensure milestone accountability.
• Proactively manage executive-level risks and issues, personally leading resolution of high-priority partner concerns with pace and transparency.
• Represent Evinova at AstraZeneca internal conferences, digital health forums and executive off-sites, positioning the partnership as a benchmark for clinical AI and digital health collaboration.
• Drive innovation at the intersection of digital health and AI by identifying, scoping and delivering joint initiatives in areas such as AI-assisted document authoring, predictive analytics for study design, decentralised trial enablement through Unify and new modalities.
• Partner with Evinova’s Chief Product Officer and technology leaders to shape the product roadmap around AstraZeneca’s unmet needs and strategic priorities, ensuring investments deliver measurable value to the pipeline.
• Lead proof-of-concept programmes that translate AI innovation into scalable, validated clinical solutions deployed across global study portfolios.
• Stay ahead of industry trends in AI, regulation and clinical technology, bringing external insight into both Evinova’s leadership team and AstraZeneca’s strategic planning.
• Design and operate an executive communication framework that provides clear visibility of programme status, milestones, risks and outcomes across AstraZeneca’s leadership layers.
• Build dashboards, scorecards and strategic briefings that give real-time insight into Evinova-enabled programmes.
• Act as editorial authority for all AstraZeneca-facing communications from Evinova, ensuring narratives are accurate, aligned and tailored from working teams through to board level.
• Coordinate with marketing and communications to amplify partnership successes, case studies and joint innovation stories in external forums.
• Own the strategic coordination of training and enablement programmes for AstraZeneca partners across clinical, IT, data and regulatory functions.
• Partner with Customer Success, Professional Services and Product teams to design structured learning pathways, role-based curricula and ongoing capability-building programmes.
• Identify and nurture a network of digital and AI champions within AstraZeneca who act as advocates and change agents for Evinova adoption across the global organisation.
• Track training coverage, proficiency levels and adoption metrics to refine enablement approaches and accelerate time-to-value for new deployments.
• Act as internal orchestrator across Evinova’s delivery, product, commercial, regulatory and customer success functions to ensure aligned workstreams that deliver on AstraZeneca programme milestones.
• Own the master programme plan for AstraZeneca, integrating timelines, dependencies and accountabilities so the customer experiences a single coordinated team rather than fragmented engagements.
• Promote a culture of proactive issue identification and cross-functional problem solving so risks are surfaced early and resolved quickly.
• Establish joint operating rhythms including programme reviews, planning sessions and escalation protocols that keep all parties focused on shared priorities.
• Provide organisational leadership by building, leading and inspiring a high-performing enterprise partnership team of around 30 specialists who are double-hatted into therapy areas, programmes and functions across clinical development.
• Define the capabilities, skills and operating model required for a world-class enterprise partnership function; recruit, develop and retain top talent in collaboration with People & Culture.
• Set OKRs, KPIs and performance governance for the function, providing regular reporting on partnership health to the Evinova Leadership Team and President.
• Contribute actively to company-wide strategy, culture and performance beyond the AstraZeneca remit, championing continuous innovation, training and transformation across one of the world’s largest pharmaceutical organisations.

Chief Digital and AI Enterprise Partner Lead

• Work with AI and ML technologies on healthcare data
• Collaborate within teams on federal healthcare programs
• Implement data governance and compliance
• Analyze AI solutions in healthcare scenarios

• Design, develop, and maintain scalable full-stack web applications using modern frameworks and cloud-native architectures
• Build and maintain user interfaces using React and related front-end technologies
• Develop backend services, APIs, serverless functions, and integrations using Python and/or Node.js
• Integrate AI and machine learning services such as Amazon Bedrock and other large language model (LLM) platforms into enterprise applications
• Implement Retrieval-Augmented Generation (RAG), agentic AI workflows, and AI orchestration capabilities
• Optimize AI model prompts, monitor model performance, and support cost-efficient AI solution design
• Support deployment automation, monitoring, troubleshooting, and operational sustainment of AI-enabled systems
• Collaborate with engineers, product owners, and stakeholders to deliver scalable and secure solutions
• Ensure compliance with cybersecurity requirements and cloud governance best practices utilizing AWS IAM, security guardrails, and related controls
• Support DevSecOps activities including CI/CD pipelines, infrastructure automation, and cloud cost management

Full Stack AI Developer

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Implement proof of concept through production-ready capabilities that directly improve clinical trial processes for a large system supporting cancer clinical trials.
• Design, build, and deploy autonomous AI agents capable of understanding high-level goals, planning actions, and executing tasks with minimal human intervention.
• Utilize frameworks like LangChain, LangGraph or CrewAI to build LLM-powered workflows.
• Build robust backend services (Python/REST API/Node.js) and Spring boot RESTful APIs to support agent orchestration and user interfaces.
• Create intuitive interfaces (React, Next.js, TypeScript) for users to monitor, configure, and intervene in AI agent workflows with human-in-the-loop.
• Develop custom tools, plugins, and Model Context Protocol (MCP) servers to securely expose internal services, APIs, and databases to LLMs.
• Architect and optimize Retrieval-Augmented Generation (RAG) pipelines and vector databases.
• Deploy AI applications on cloud platforms (AWS, Azure, or GCP) using Docker, Kubernetes, and CI/CD pipelines.
• Implement observability, logging, tracing (e.g., LangSmith, Langfuse), and guardrails to monitor AI performance, cost, and safety.

AI Engineer jobs in Maryland

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Senior Director, AI Engineering

AI Engineer

AI Workflow Engineer

AI Engineer

Senior Software Engineer – Applied AI

AI Engineer – SME

Senior AI Software Engineer

AI Engineer

Enterprise AI Architect

Senior Engineer, Interactive Voice Response – AI/ML

AI & Automations Lead

AI Software Engineer – Lead

Manager, Global AI Enterprise Architect

Senior Product Manager, AI Platform

Chief Digital and AI Enterprise Partner Lead

AI Engineer

Full Stack AI Developer

QuantAI Engineer – Trading, Investment & Optimization

Full Stack AI Developer