AI Engineer

• De sign, de velop, and fine-tune a variety of AI models
• Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration
• Build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
• Implement end-to-end data pipelines, ETL processes, and back-end services (Python, C/C++, Java) that feed data to models
• Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning
• Maintain model registries, monitoring, and version control of context protocols
• Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments
• Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation
• Stay current with emerging LLM architectures, agentic paradigms, PEFT/LoRA methods, and AI-safety techniques; translate new research into operational capabilities

AI Engineer – Mission Innovation Lab

• Ship and debug code on a live, real-time voice pipeline
• 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

Senior Software Engineer – Applied AI

• 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

• Provide governance, oversight, and controlled expansion of internal AI tool
• Execute quarterly, risk-based testing program for independent credible challenge
• Collaborate with various stakeholders to review AI initiatives and escalate risks
• Evaluate controls across the AI lifecycle
• Translate regulatory requirements into AI-focused testing procedures
• Develop credible challenge documentation and reporting for governance committees
• Enhance AI testing frameworks and tools

Lead AI Compliance Testing

• 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

• Design, train, and deploy robust policies for locomotion, manipulation, and dynamic interactions with the environment.
• Develop core reinforcement learning infrastructure, including scalable training pipelines and evaluation frameworks.
• Design and implement new simulation environments and tasks to support training and deployment of control policies.
• Develop, design, and test imitation learning methods
• Collaborate with Robotics Software and AI engineering teams to develop policies which can be transferred to production

Senior AI Research Engineer

• 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 the development and implementation of quality management frameworks across Supply Chain Technology systems, ensuring compliance, reliability, and performance
• Oversee digital quality programs, including system validation, testing, and continuous improvement initiatives aligned with business and regulatory requirements
• Drive the integration of AI and advanced analytics into quality systems to enhance monitoring, predictive insights, and process optimization
• Establish and monitor quality metrics, dashboards, and scorecards to evaluate system performance and identify opportunities for improvement
• Partner with cross-functional teams (IT, Supply Chain, Quality, Regulatory) to ensure alignment on quality standards and execution priorities
• Lead risk management activities, including assessment of system controls, mitigation strategies, and issue escalation processes
• Guide process improvement initiatives, leveraging methodologies such as Lean, Six Sigma, or continuous improvement practices
• Ensure adherence to regulatory and compliance standards, including auditing systems, interpreting regulations, and implementing required changes
• Manage and develop a team, providing coaching, performance feedback, and career development support
• Foster a culture of quality, innovation, and accountability aligned with organizational values and business goals.

Technology Manager, Quality Systems – AI Lead

• Lead the development and implementation of quality management frameworks across Supply Chain Technology systems, ensuring compliance, reliability, and performance
• Oversee digital quality programs, including system validation, testing, and continuous improvement initiatives aligned with business and regulatory requirements
• Drive the integration of AI and advanced analytics into quality systems to enhance monitoring, predictive insights, and process optimization
• Establish and monitor quality metrics, dashboards, and scorecards to evaluate system performance and identify opportunities for improvement
• Partner with cross-functional teams (IT, Supply Chain, Quality, Regulatory) to ensure alignment on quality standards and execution priorities
• Lead risk management activities, including assessment of system controls, mitigation strategies, and issue escalation processes
• Guide process improvement initiatives, leveraging methodologies such as Lean, Six Sigma, or continuous improvement practices
• Ensure adherence to regulatory and compliance standards, including auditing systems, interpreting regulations, and implementing required changes
• Manage and develop a team, providing coaching, performance feedback, and career development support
• Foster a culture of quality, innovation, and accountability aligned with organizational values and business goals

Technology Manager, Quality Systems, AI Lead

• Developing, implementing, and maintaining AI-driven solutions to enhance the efficiency, accuracy, and scalability of pharmaceutical packaging operations
• Collaborating with cross-functional teams, including operations, quality assurance, and regulatory compliance, to integrate AI technologies
• Designing and deploying AI/ML algorithms to mine and analyze MES and data lake data
• Building intuitive dashboards and reporting tools for actionable insights
• Developing data pipelines that integrate and normalize data across packaging lines
• Applying statistical and machine learning techniques to identify root causes of rejects and downtime
• Developing predictive maintenance algorithms to forecast failures
• Supporting equipment and OT vendors in configuring and tuning AI/ML modules
• Partnering with Quality and Regulatory teams to ensure compliance with standards
• Monitoring, retraining, and documenting deployed models for accuracy

• Design and build AI-powered applications and integrations across a variety of use cases
• Lead implementation of small- to medium-scale AI solutions, with contribution to larger enterprise initiatives
• Translate business requirements into scalable, supportable technical architectures
• Design and implement data pipelines for AI/ML use cases (ingestion, transformation, and delivery)
• Prepare and manage datasets for AI consumption, including cleansing, normalization, and feature engineering
• Ensure data quality, lineage, and readiness for model training and inference
• Apply best practices for handling sensitive data, including PII and regulated data
• Evaluate and apply appropriate AI/ML (regression models, NLP, LLMs, Multi-modal models, embedding models, etc.) approaches based on use case.
• Design and implement retrieval systems, including Retrieval-Augmented Generation (RAG)
• Apply agentic AI patterns and architectures to enable multi-step reasoning and orchestration
• Support training, fine-tuning, and optimization of models where applicable
• Implement techniques such as model distillation, quantization, and performance tuning
• Balance cost, performance, and accuracy in model selection and deployment
• Build and integrate AI capabilities into enterprise systems, APIs, and workflows
• Design scalable, secure architectures for AI services and applications
• Ensure solutions are production-ready, maintainable, and aligned to enterprise standards
• Engage with business stakeholders to gather and refine requirements
• Clearly articulate technical concepts, trade-offs, and solution approaches
• Validate requirements and translate them into actionable technical plans
• Act as a bridge between business and engineering teams

• At PNC, our people are our greatest differentiator and competitive advantage
• We work together each day to foster an inclusive workplace culture
• As a Software Engineer Senior, you will be based in Dallas, TX or Pittsburgh, PA
• PNC is seeking an AI Engineer with strong expertise in building and deploying LLM-powered, production-grade systems
• Requires end-to-end ownership across model development, optimization, and real-world integration
• Proficiency with modern inference frameworks (vLLM, TGI, Triton)
• Proven ability to build and manage pipelines across training, deployment, monitoring, and evaluation
• Design AI agents and multi-agent workflows, balancing model reasoning with deterministic business logic

Senior Software Engineer – AI Engineer

• Leading, mentoring, and growing a global team of software engineers and Forward Deployed Engineers, fostering a high-performance, artificial intelligence–first culture.
• Driving adoption of artificial intelligence across the software development lifecycle to improve engineering productivity, quality, and velocity.
• Managing team execution, delivery, hiring, performance management, and career development while ensuring operational excellence.
• Partnering with Product Management and senior engineering leaders to define and deliver scalable artificial intelligence agent platforms and enterprise capabilities.
• Designing and contributing to cloud-native solutions leveraging Microsoft Azure, Python, and C# technologies.
• Building and leading a Forward Deployed Engineering function that collaborates directly with customers to validate use cases and accelerate artificial intelligence adoption.
• Translating customer feedback and real-world implementations into reusable platform features and product enhancements.

Product Development Manager – AI Platform

• Design, develop, deploy, and maintain Agentic AI solutions that automate business processes and complex workflows.
• Build autonomous and semi-autonomous AI agents capable of reasoning, planning, tool usage, retrieval, memory management, and multi-step task execution.
• Develop and deploy multi-agent systems that collaborate to accomplish business objectives.
• Integrate Large Language Models (LLMs) from providers such as OpenAI, Anthropic, Google, Azure OpenAI, and open-source alternatives.
• Build Retrieval-Augmented Generation (RAG) solutions leveraging vector databases and enterprise knowledge sources.
• Design and implement agent orchestration frameworks using technologies such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, or similar platforms.
• Develop APIs, microservices, and backend services that support AI-driven applications and workflows.
• Integrate AI agents with enterprise systems including CRM, ERP, ticketing platforms, collaboration tools, databases, and third-party SaaS applications.
• Implement robust observability, monitoring, evaluation, logging, guardrails, and governance frameworks for AI solutions.
• Collaborate with business stakeholders to identify automation opportunities and determine where AI agents can deliver measurable value.
• Evaluate emerging AI technologies and recommend practical approaches for implementation.
• Perform hands-on development using Agile and Scrum methodologies.
• Continuously optimize deployed AI systems for performance, reliability, cost efficiency, and user adoption.
• Troubleshoot production AI applications and rapidly resolve operational issues.
• Serve as a technical advisor on AI automation strategy, architecture, and implementation best practices.

• Participate and effectively contribute to the design, development, and implementation of complex applications, often using new technologies
• Provide technical expertise and systems design for individual initiatives
• Lead the architecture and design of enterprise data, analytics, and AI solutions leveraging SAP Datasphere, SAP Analytics Cloud (SAC), and SAP BTP
• Define data architecture standards, governance models, integration strategies, and best practices
• Design scalable and secure enterprise data models supporting reporting, analytics, planning, forecasting, and AI use cases
• Develop architecture roadmaps aligned with organizational digital transformation strategies
• Architect data pipelines integrating SAP and non-SAP source systems
• Optimize data performance, scalability, and security within Datasphere environments
• Support integration between SAC, Datasphere, SAP S/4HANA, BW/4HANA, and third-party data sources
• Lead the architecture and integration of AI/ML capabilities within SAP environments
• Collaborate with data science teams to operationalize AI/ML models within enterprise workflows
• Guide development teams on APIs, data services, event processing, and cloud-native integration patterns
• Ensure enterprise security, compliance, and performance standards are met
• Develop and maintain architecture diagrams, standards, technical specifications, and operational procedures

SAP Datasphere, SAC, AI Architect

• Contribute to the architecture and take ownership of the design, development, and deployment of scalable Generative AI and Machine Learning systems into production environments.
• Develop end-to-end GenAI features, including backend API services, model integration, model monitoring, evaluations, and deployments.
• Integrate and optimize LLMs for specific business planning use cases, including prompt engineering and RAG implementation.
• Build cutting-edge conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language.
• Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics.
• Design and develop APIs that expose AI capabilities to Anaplan's platform and third-party integrations.
• Optimize model inference pipelines for performance, cost, and scalability in production environments.
• Implement monitoring, logging, and observability for GenAI systems to track usage, errors, and model behavior.
• Participate in code reviews, lead technical design discussions, and mentor junior and mid-level engineers.

Senior AI Engineer

• Building and deploying LLM-powered, production-grade systems
• End-to-end ownership across model development, optimization, and real-world integration
• Proposing & designing software solutions to address complex business needs
• Writing code, tests, and deploying software
• Preparing technical and procedural documentation required for software
• Maintaining and debugging software

• Lead and develop a blended team of engineers (onshore and offshore), providing coaching, performance management, and career development while driving accountability for results
• Own end-to-end delivery of cloud-based and AI/ML platform initiatives, including continued development and scaling of capabilities such as AWS SageMaker and internally developed solutions
• Translate business and technology priorities into actionable delivery plans, including scope, dependencies, risks, and resource needs across a shared services model supporting multiple lines of business
• Establish and maintain strong delivery discipline through structured planning, tracking, and reporting (e.g., JIRA or similar tools) to ensure predictable and reliable outcomes
• Provide clear, data-driven communication to stakeholders and leadership on delivery status, timelines, risks, and overall progress
• Partner with engineering, product, and business teams to align on priorities, enable platform adoption, and ensure successful execution of cross-functional initiatives
• Proactively identify and mitigate risks, resolve blockers, and drive continuous improvement in delivery processes and team effectiveness
• Monitor and report on delivery performance, ensuring alignment with organizational goals and the scalability of platform capabilities
• Support operational planning, including staffing, resource allocation, and budget considerations
• Ensure adherence to technology, risk, and security standards while delivering high-quality, scalable solutions

Technology Delivery Manager – AI Platforms

• Lead the development, deployment, and ongoing optimization of AI and Generative AI solutions
• Implement engineering best practices to ensure scalability and uptime
• Track and report on platforms performance and availability metrics
• Be involved in all stages of the model development lifecycle from design to development, testing, and deployment
• Collaborate with software engineers, data scientists, and DevOps teams to design and implement technical designs and solutions
• Stay up-to-date with new technologies and industry trends

Lead AI Engineer

• Define and execute the AI architecture roadmap for Advice & Wealth Management in partnership and collaboration with other organizations
• Establish architecture patterns for both traditional ML and generative AI, including deployment and monitoring
• Help business and product leaders understand how they can leverage AI to solve their business problems
• Influence enterprise guardrails and standards for responsible AI usage (evaluation, observability, model performance, and risk management)
• Establish repeatable patterns for integrating AI into applications, workflows, and platforms
• Partner with data architecture to ensure AI-ready data foundations (quality, governance, lineage)
• Guide teams on build vs. buy vs. partner decisions for AI capabilities and platforms
• Research emerging AI technologies and patterns; translate into practical recommendations and investment proposals
• Influence senior leadership on AI strategy, investment priorities, and sequencing
• Coach and mentor architects and senior engineers in AI architecture and design practices

AI Engineer jobs in Pennsylvania

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AI Engineer – Mission Innovation Lab

Senior Software Engineer – Applied AI

Product Engineer – AI

Lead AI Compliance Testing

AI Engineer

Senior AI Research Engineer

AI & Automations Lead

Technology Manager, Quality Systems – AI Lead

Technology Manager, Quality Systems, AI Lead

AI Engineer

AI Engineer

Senior Software Engineer – AI Engineer

Product Development Manager – AI Platform

AI Engineer

SAP Datasphere, SAC, AI Architect

Senior AI Engineer

Senior Software Engineer – AI Engineer

Technology Delivery Manager – AI Platforms

Lead AI Engineer

Chief AI Architect