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.

Machine Learning Engineer

• The Machine Learning Engineer will perform their job duties to a high standard, working both independently and collaboratively.
• The core responsibility is to assist in the development, implementation, testing, and evaluation of an AI-based Real-Time Mesoscale Analysis (AI-RTMA) system in support of NOAA’s National Blend of Models (NBM).
• The AI-RTMA system will generate high spatial and temporal resolution analyses of meteorological variables to reduce biases in the NBM fields.
• Conduct a comprehensive review of state-of-the-art AI-based data assimilation and end-to-end weather forecasting methodologies, systems, and frameworks.
• Communicate findings with EMC scientists and external partners to inform the development of a scientifically robust and efficient AI-RTMA approach.
• Collaborate with NOAA’s NBM team and key stakeholders to define product requirements for AI-RTMA, including domain configuration, grid structure, output variables, spatial and temporal resolution, and data formats suitable for operational evaluation and transition.
• Design, implement, and maintain robust data pipelines to support AI-RTMA training, validation, testing, and evaluation. This includes collecting, formatting, quality-controlling, and integrating diverse observational datasets (e.g., conventional observations, satellite, radar, and other sources), as well as preparing model inputs, targets, metadata, and training/validation splits.
• Develop, train, rigorously test, and deploy a fully functional AI-RTMA system based on selected AI frameworks or architectures.
• Implement cross-validation and other evaluation methodologies to quantify model performance and reliability during inference.

• 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

AI Research Scientist

• Conduct fundamental and innovative development in low-cost yet powerful vision-language models (VLM), unified models, automatic model compression, optimization and deployment on cloud and edge.
• Design or implement state-of-the-art techs on model compression, inference speedup, deployment on hardwares, tool automation.
• PoC for various vision+text, generation relevant tasks (VQA, captioning, understanding, etc) and hardwares.
• Contribute to library and tool development to support business; or Publish influential research in top-tier conferences and journals.

Research Intern, Vision Foundation Model and Generative AI

• 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

LLM Engineer

• 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

• 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 optimize AI and ML solutions to enhance operational and analytical capabilities within a larger enterprise level Data and AI analytics platform.
• Build and maintain data pipelines for efficient data processing and model training.
• Train and tune algorithms to improve predictive accuracy and decision-support tasks.
• Deploy models into production environments, ensuring reliability and performance.
• Identify and integrate appropriate COTS, government, and custom tools within established frameworks.
• Collaborate with cross-functional teams to ensure alignment with enterprise architecture and security requirements.
• Manage project timelines and deliverables, ensuring adherence to quality standards.
• Facilitate communication between technical teams and stakeholders to align project goals.

Machine Learning Engineer jobs in Maryland

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Senior Director, AI Engineering

AI Engineer

AI Workflow Engineer

AI Engineer

Machine Learning 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

Research Intern, Vision Foundation Model and Generative AI

Chief Digital and AI Enterprise Partner Lead

Senior MLOps, Generative AI Engineer

AI Engineer

SME AI/ML Engineer