AI Engineer

• Lead OMG AI/ML and automation strategy, architecture, technical direction, priorities, and adoption across PIC, ASIC, TROSA, and Pluggable technologies
• Build AI/ML capability across OMG through technical guidance, mentoring, reusable examples, and coordinated learning opportunities
• Establish common methods, governance, knowledge systems, and reusable architecture patterns for scalable, traceable, and responsible use of AI/ML
• Transform engineering data, workflows, knowledge, and traceability into AI-ready systems spanning development, qualification, manufacturing, reliability, and field performance
• Guide engineers in selecting, developing, validating, and using AI/ML methods and reusable solutions, including agents, skills, RAG, analytics, and automation
• Partner across Development, Product Engineering, Test, Reliability, Quality, Manufacturing, Operations, IT, and Nokia AI/ML communities
• Define and maintain the OMG AI/ML technology roadmap and align it with Nokia AI strategy, architecture direction, and long-term business objectives
• Represent OMG in Nokia-wide AI, data, automation, and architecture forums
• Establish AI/ML governance, architecture standards, and engineering practices for adoption across Nokia engineering organizations
• Lead evaluation and selection of AI platforms, cloud technologies, external partners, implementation approaches, and strategic technology investments
• Develop OMG AI/ML competency frameworks, workforce development plans, training strategies, and technical communities
• Serve as a senior technical advisor to OMG leadership
• Provide strategic leadership for cloud platform initiatives, including staffing, contributor selection, priorities, deliverables, risks, and decisions

Senior Principal AI Architect

• Design, develop, test, implement, document, maintain, and support ServiceNow solutions for Empyrean
• Enhance IT Service Management and business processes through platform development, workflow automation, integrations, and AI-enabled capabilities
• Collaborate with cross-functional teams to design, configure, troubleshoot, and implement applications and solutions
• Build service catalog offerings, workflows, self-service capabilities, and Flow Designer automations
• Integrate ServiceNow with enterprise systems and manage associated data exchange
• Develop and maintain business rules, assignment and approval rules, client scripts, flows, and other platform components
• Evaluate whether AI, intelligent automation, traditional workflow automation, or standard ServiceNow configuration best fits business needs
• Configure, integrate, test, implement, and support approved ServiceNow AI and intelligent automation capabilities
• Identify automation and AI-enabled improvement opportunities and provide technical input on feasibility, dependencies, risks, and implementation needs
• Contribute to proofs of concept, pilots, project planning, estimation, delivery, code reviews, knowledge sharing, upgrades, and continual improvement
• Organize work through initiatives, epics, stories, and tasks; communicate progress, dependencies, and risks
• Monitor, troubleshoot, and improve ServiceNow and AI-enabled capabilities after launch
• Participate in an on-call rotation and provide after-hours support for production incidents and service disruptions
• Perform other job-related duties as assigned

ServiceNow Developer, AI & Intelligent Automation

• Own technical management of all Oracle Fusion environments, including scheduled environment refreshes, instance cloning, environment usage, and configuration baselines
• Participate in preparation and execution of Oracle’s mandatory quarterly cloud updates, including pre-update checks and post-update technical smoke testing
• Own non-functional requirements for the Oracle Cloud environment, including security architecture, role design governance, performance benchmarking, and technical configuration control
• Administer security for Oracle Fusion Applications, PaaS, and DBaaS platforms
• Design, build, and deploy custom Oracle Fusion security roles while enforcing least privilege, Segregation of Duties, and Sensitive Access compliance
• Serve as technical owner of Oracle Fusion Risk Management Cloud
• Configure, deploy, and maintain Advanced Access Controls and Advanced Financial Controls to detect and mitigate financial risks, fraud, and policy violations
• Translate audit requirements into effective platform controls
• Provide Level 2/3 technical support for environment issues, access problems, and deployment failures
• Conduct root cause analysis for platform-level incidents and serve as an escalation point
• Evaluate, design, and deploy AI-driven tools, Model Context Protocol implementations, Terraform automation scripts, and security artifacts
• Enhance platform security operations, threat monitoring, and governance workflows across the Oracle Cloud ecosystem

Senior Oracle Security, AI Engineer

• Design and implement retrieval-augmented generation (RAG) pipelines to ground AI responses in enterprise knowledge sources
• Develop AI models to enhance customer service automation capabilities with measurable improvements
• Apply prompt engineering and fine-tuning techniques to optimize AI system performance
• Create AI tools that support integration of AI-driven improvements into operational frameworks
• Collaborate with cross-functional teams to integrate AI systems effectively
• Design AI systems that improve interaction capabilities within complex service workflows
• Contribute to strategic AI initiatives advancing customer satisfaction and efficiency
• Configure GenAI platforms such as Claude, OpenAI, and Glean
• Establish pilot groups and manage controlled feature rollouts from test groups to enterprise-wide deployment
• Build systems that scan company agents, identify policy drift, excessive token use, and deprecated models, and deploy fixes at scale
• Own model evaluation harnesses, model cards, and ongoing model risk checks
• Perform other duties and projects as assigned by business management

• Lead the architecture and technical direction for enterprise AI and machine learning solutions
• Design, build, and deploy AI applications, LLM-powered products, RAG pipelines, and agent-based systems
• Establish best practices for model development, evaluation, deployment, monitoring, and governance
• Drive AI platform strategy, scalability, reliability, security, and performance
• Partner with Engineering, Product, Security, and Data teams to translate business needs into AI-powered solutions
• Evaluate emerging AI technologies and recommend adoption opportunities
• Mentor engineers and provide technical leadership across AI initiatives
• Ensure AI solutions align with security, privacy, and compliance requirements
• Shape AI strategy, influence enterprise technology decisions, and build innovative AI solutions with measurable business impact

Principal AI Engineer

• Design, develop, and deploy AI-powered applications and platforms supporting enterprise business objectives
• Build Generative AI solutions using LLMs, RAG architectures, and agent-based workflows
• Develop intelligent assistants, copilots, chatbots, and AI automation solutions
• Transform AI concepts, proofs of concept, and prototypes into production-ready software products
• Design and build scalable APIs, microservices, and backend services
• Integrate AI capabilities into enterprise platforms, business systems, and customer-facing applications
• Develop secure, reliable, and reusable components within modern software architectures
• Translate business requirements into technical solutions with cross-functional teams
• Deploy and manage AI workloads across Azure and AWS
• Implement containerized and cloud-native solutions using Kubernetes and modern orchestration technologies
• Build and maintain enterprise-grade AI platforms for high-volume production workloads
• Optimize performance, reliability, security, and scalability of AI services
• Establish and maintain CI/CD pipelines for AI-enabled applications and services
• Implement MLOps practices for deployment, monitoring, testing, and lifecycle management
• Support model integration, version management, governance, and operational excellence
• Monitor production environments and improve platform performance and user experience
• Evaluate emerging AI technologies and opportunities for enterprise adoption
• Contribute to technical architecture decisions and AI engineering best practices
• Drive continuous improvement across AI development methodologies and delivery frameworks

Principal AI Engineer – SDLC

• Design, develop, and deploy AI-powered applications and platforms supporting enterprise business objectives
• Build Generative AI solutions using LLMs, RAG architectures, and agent-based workflows
• Develop intelligent assistants, copilots, chatbots, and AI automation solutions
• Transform AI concepts, proofs of concept, and prototypes into production-ready software products
• Design and build scalable APIs, microservices, and backend services
• Integrate AI capabilities into enterprise platforms, business systems, and customer-facing applications
• Develop secure, reliable, and reusable application components
• Partner with cross-functional teams to translate business requirements into technical solutions
• Deploy and manage AI workloads across Azure and AWS
• Implement containerized and cloud-native solutions using Kubernetes and modern orchestration technologies
• Build and maintain enterprise-grade AI platforms for high-volume production workloads
• Optimize performance, reliability, security, and scalability of AI services
• Establish and maintain CI/CD pipelines
• Implement MLOps practices for deployment, monitoring, testing, and lifecycle management
• Support model integration, version management, governance, and operational excellence
• Monitor production environments and improve platform performance and user experience
• Evaluate emerging AI technologies and identify enterprise adoption opportunities
• Contribute to technical architecture decisions and AI engineering best practices
• Drive continuous improvement across AI development methodologies and delivery frameworks

• Design, build, and train machine learning and deep learning models, including GenAI, NLP, and predictive analytics solutions for healthcare applications
• Develop, test, and deploy AI solutions in cloud and on-premise environments
• Work with large healthcare datasets, including data preprocessing, feature engineering, and model training
• Ensure compliance with HIPAA and other regulatory standards
• Implement and optimize AI models within Gainwell’s existing technology stack
• Collaborate with software engineers to ensure seamless system integration
• Monitor, refine, and optimize AI models for accuracy, efficiency, and speed using MLOps best practices
• Stay current with AI/ML advancements and explore new technologies and methodologies
• Ensure AI implementations adhere to healthcare regulations, ethical AI principles, and data privacy standards
• Identify opportunities to automate workflows and optimize business processes using AI-driven solutions