AI Engineer

• Responsible for architecting solutions achieving customer business outcomes
• Develop and articulate compelling, accurate, and relevant proposals
• Provide technical expertise to sales teams and customers
• Develop customer proposals and critically review them
• Quantify the impact of business problems and position business value
• Communicate how the solution value propositions address customer business needs
• Drive the Account Business Planning process leveraging knowledge of industry trends and customer’s technical environment

AI Architect – Pre-Sales

• Lead the design, development, and deployment of production AI systems end-to-end
• Contribute hands-on to the codebase and set a high bar for engineering quality
• Establish scalable patterns for evaluation, deployment, and system reliability
• Conduct technical design reviews and guide architectural decisions
• Mentor and develop engineers, providing regular coaching and technical guidance
• Support hiring efforts, including candidate evaluation and onboarding
• Partner with cross-functional stakeholders to align priorities and deliver impactful solutions
• Help translate experimentation into stable, production-ready systems

Full Stack AI Engineer

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Own the product roadmap covering agentic execution environments, skills and MCP ecosystem, agent orchestration, and AI-powered code review
• Drive developer adoption through training, newsletters, cross-team projects, and feedback sessions
• Define and track success metrics
• Coordinate across teams as the product voice spanning various areas of the Developer Experience data platform
• Shape vendor strategy: evaluate external AI tooling
• Own ADAS ecosystem discovery by mapping tools, workflows, build systems, and dependencies
• Build the business case for convergence by quantifying dual-ecosystem costs
• Identify AI acceleration opportunities
• Communicate to leadership: translate technical concepts into briefings

Staff Product Manager, AI Developer Productivity

• Design, build, and deploy generative and agentic AI solutions that automate, optimize, and transform tools supporting Medtronic’s global operations
• Collaborate closely with cross-functional partners to implement intelligent automation, architect AI-powered workflows, and develop scalable, robust APIs and platforms that drive operational efficiency and enhance staff experience across global facilities
• Support Medtronic’s strategic objectives by leveraging advanced AI / Machine Learning (ML) frameworks and modern development practices to deliver measurable business impact
• Ensure contributions align with the company's mission to alleviate pain, restore health, and extend life

Principal Artificial Intelligence Engineer

• Identify opportunities to apply AI, machine learning, and automation to improve manufacturing throughput, quality, and cost efficiency
• Lead end-to-end project management for AI initiatives, from concept through deployment and continuous improvement
• Develop, test, and deploy AI models and tools (e.g., predictive maintenance, demand forecasting, quality inspection, workflow automation)
• Collaborate with production, engineering, and IT teams to integrate AI solutions into existing systems and processes
• Analyze operational data to identify trends, inefficiencies, and opportunities for optimization
• Translate business needs into technical requirements and actionable solutions
• Manage project timelines, budgets, vendor relationships, and stakeholder communication
• Build dashboards, reporting tools, and data pipelines to support real-time decision-making
• Ensure solutions are scalable, secure, and aligned with organizational goals
• Provide training and change management support to drive adoption across the workforce
• Stay current on emerging AI technologies and recommend practical applications for the business

AI Developer – Project Manager

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Own and articulate the AI vision for Global Finance, aligned with Dematic’s enterprise digital, data, and automation strategy
• Define, maintain, and evolve the roadmap for financial artificial intelligence, balancing near-term impact with long-term capability development
• Translate executive leadership priorities into a prioritized portfolio of AI-enabled initiatives
• Continuously assess emerging AI capabilities and their applicability to Finance use cases
• Partner with Finance teams (FP&A, Function Controlling, Project Management, Accounting, etc.) to identify AI-ready opportunities
• Translate Finance workflows, decision processes, and challenges into clear, actionable AI use cases
• Develop case studies and benefit analysis models, including efficiency gains, cost savings, risk reduction, and decision-quality improvements
• Act as the primary liaison between Finance process owners and AI technical teams, ensuring shared understanding of requirements and constraints
• Ensure Finance domain knowledge, controls, and business logic are embedded into AI solution development
• Advocate for practical, usable solutions that resolve real problems and fit Finance ways of working
• Own AI governance for Finance, including data privacy, model risk management, audit readiness, and regulatory compliance
• Ensure AI solutions align with financial controls, internal governance standards, and external regulations
• Establish Finance AI standards, policies, and operating models for responsible and ethical AI use
• Partner with Risk, Compliance, IT, and Internal Audit to ensure transparency and control
• Coordinate pilot programs and User Acceptance Testing (UAT) with Finance end users
• Validate outputs, capture feedback, and ensure solutions meet business and control requirements
• Lead change management, training, and AI literacy across the Finance organization
• Develop user materials, job aids, and communication plans to support adoption and behavioral change
• Define and analyze key performance indicators and value realization metrics for Finance AI initiatives
• Assess live AI solutions to ensure continued performance, accuracy, and adoption
• Surface issues, risks, and improvement opportunities to technical and Finance leadership
• Provide regular executive updates to leadership on progress, benefits, and roadmap
• Partner closely with IT, Digital, and Data teams on AI architecture, platforms, security, and scalability
• Ensure Finance AI solutions leverage enterprise-aligned, secure, and reusable technology foundations
• Balance innovation speed with sustainability, control, and integration

Finance AI Lead

• Design, develop, and deploy machine learning models and AI solutions tailored to Finance use cases (e.g., forecasting, planning, variance analysis, anomaly and risk detection).
• Build and maintain ML models for financial planning, forecasting, trend analysis, and anomaly detection across large, structured datasets.
• Develop LLM‑powered tools to support financial analysis, commentary generation, summarization, and scripted insights for Finance users.
• Translate Finance requirements into data pipelines, feature engineering, model architecture, and deployment approaches.
• Conduct model validation, back‑testing, and performance evaluation to ensure accuracy, robustness, and business relevance.
• Evaluate model performance over time and diagnose issues related to data quality, concept drift, and changing business conditions.
• Implement appropriate controls, explain-ability, and documentation to support Finance governance, audit, and compliance requirements.
• Document model assumptions, methodologies, limitations, and change history for audit and risk review.
• Implement MLOps standard methodologies, including model versioning and lifecycle management, drift detection and performance monitoring, retraining schedules and automated pipelines, and reproducibility and rollback procedures.
• Partner with IT to deploy models into enterprise environments (cloud, Salesforce, SAP, Snowflake, proprietary tools, etc.).
• Ensure AI solutions are secure, scalable, and maintainable within enterprise standards.
• Collaborate with Finance, IT, data teams, and other AI workstreams to promote consistent standards, tooling, and patterns across the organization.
• Serve as a technical thought partner to Finance leaders, helping shape the AI roadmap and identify high‑value use cases.
• Help educate Finance partners on AI capabilities, limitations, and responsible usage.
• Contribute to establishing foundational AI practices for a growing Finance AI team.

Senior Applied AI Engineer – Finance

• Design, develop, and deploy machine learning models and AI solutions tailored to Finance use cases (e.g., forecasting, planning, variance analysis, anomaly and risk detection).
• Build and maintain ML models for financial planning, forecasting, trend analysis, and anomaly detection across large, structured datasets.
• Develop LLM‑powered tools to support financial analysis, commentary generation, summarization, and scripted insights for Finance users.
• Translate Finance requirements into data pipelines, feature engineering, model architecture, and deployment approaches.
• Conduct model validation, back‑testing, and performance evaluation to ensure accuracy, robustness, and business relevance.
• Evaluate model performance over time and diagnose issues related to data quality, concept drift, and changing business conditions.
• Implement appropriate controls, explain-ability, and documentation to support Finance governance, audit, and compliance requirements.
• Partner with IT to deploy models into enterprise environments (cloud, Salesforce, SAP, Snowflake, proprietary tools, etc.).
• Ensure AI solutions are secure, scalable, and maintainable within enterprise standards.

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Design, develop, and maintain data-driven and AI-enabled applications and services that support Product Development engineering teams.
• Write high-quality, performant SQL (queries, functions, stored procedures) for complex data transformations and modeling across enterprise data platforms (e.g., SQL Server, Oracle, PostgreSQL).
• Build and optimize data pipelines and workflows in Databricks (DBX) and related tools to support batch and near real-time data processing.
• Develop backend services and APIs in Java and/or Python that integrate data, business rules, and user workflows into robust, reusable components.
• Develop enterprise-grade applications using Kubernetes/Docker, Quarkus, Java, Angular, PostgreSQL, and other GM-approved tools.
• Partner with data science and AI teams to productionize AI/ML and LLM based solutions, including feature pipelines, inference integrations, monitoring, and continuous improvement.
• Proactively identify and remediate issues related to code quality, patterns, performance, security, and data correctness, using code quality analysis tools and remediation techniques.
• Lead or contribute to solution design, including architecture, patterns, and technology choices aligned with GM standards and Statement of Technical Direction.
• Apply and champion software engineering best practices, including code reviews, automated testing, branching strategies, CI/CD pipelines (e.g., GitHub Actions), observability, and secure coding practices.
• Collaborate with cross-functional teams (product owners, data engineers, architects, business stakeholders) to refine requirements, define acceptance criteria, and deliver incremental value in an Agile/SCRUM environment.
• Troubleshoot and resolve production issues across the full stack (data, application, infrastructure), driving root cause analysis, stable fixes, and clear documentation.
• Navigate complex corporate processes to remove project roadblocks and keep delivery on track.
• Provide guidance and mentoring to fellow software engineers and team members, helping grow skills in SQL, Databricks, cloud, and AI-related technologies.
• Document software and data solutions and ensure technical documentation meets GM standards and can be leveraged across VEQ and PD Business Systems.

Senior Software Engineer – AI Engineering, Productivity

• Support Ford's AI and ML engineering capability within the TOP platform, including model fine-tuning oversight, agentic orchestration architecture, and LLM evaluation
• Oversee vendor fine-tuning of Google Cloud Vertex AI using Ford proprietary diagnostic data, ensuring compliance with Ford's IP protection requirements and model weight storage architecture
• Design and build Ford's Orchestration Layer. The integration framework that connects external AI engine with other Ford internal AI engines and TOP platform services
• Evaluate AI engine outputs against defined accuracy, latency, and first-time fix rate metrics; drive iterative improvement through structured feedback loops
• Define model evaluation frameworks and acceptance criteria for AI-generated triage recommendations, ensuring clinical accuracy before dealer-facing deployment
• Build internal Ford tooling for model monitoring, drift detection, and retraining triggers within Ford's GCP environment
• Collaborate with Ford's data engineering team to define data preparation and feature engineering requirements that support model fine-tuning and inference quality
• Partner with the Ford GCP Cloud Engineers to ensure model artifact storage, versioning, and access controls comply with Ford's IP and security policies
• Contribute to the long-term insourcing roadmap by documenting model architectures, training pipelines, and prompt frameworks in sufficient detail to enable internal replication
• Represent AI and ML engineering in architecture reviews and vendor technical discussions.

Machine Learning, AI Developer

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization.
• You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.
• This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning.
• Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.
• You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Senior AI Engineer, Agentic Evaluation – V&V

• Own the full lifecycle of AI-powered applications, including requirements definition, architecture, development, deployment, and ongoing evolution.
• Design and develop modern, performant web applications using React, with a focus on usability, accessibility, and clarity of experience.
• Build and maintain backend services using Python (Flask required), ensuring scalable, well-structured API design.
• Develop AI-native application features, including RAG-based workflows, conversational interfaces, and intelligent document generation.
• Design and implement event-driven, agentic workflows that respond to real-world signals (e.g., emails, meetings, documents) and trigger appropriate system actions or user prompts.
• Build systems that extract structured insights from unstructured data sources (natural language, documents, communications) to support downstream workflows and decision-making.
• Integrate AI/LLM services (e.g., OpenAI, Anthropic) into production systems with attention to performance, cost, and reliability.
• Collaborate with data developers and knowledge experts to translate pipelines and machine learning outputs into user-facing capabilities.
• Partner directly with stakeholders across the firm to define problems, prototype solutions, and iteratively deliver meaningful outcomes.
• Contribute to the development of shared platform capabilities, including component libraries, authentication patterns, and developer tooling.
• Implement and maintain CI/CD pipelines, containerized deployments, and cloud infrastructure (Azure preferred).
• Ensure all applications align with HED governance standards, including security, authentication, observability, and responsible AI practices.
• Monitor application health, troubleshoot production issues, and maintain clear documentation and operational runbooks.
• Actively contribute to a culture of innovation, experimentation, and continuous improvement across the Data & AI Platform team.
• Participate in structured technical reviews and cross-functional governance processes; contribute to architecture decision records (ADRs), operational runbooks, and review artifacts.
• Champion production-grade engineering practices including API versioning, error handling, automated testing, and scalability patterns.

See all jobs on JobTailor

AI Architect – Pre-Sales

Full Stack AI Engineer

Full Stack AI Engineer

Senior Manager, AI Lead – Semantic Layer

Staff Product Manager, AI Developer Productivity

Principal Artificial Intelligence Engineer

AI Developer – Project Manager

Senior Software AI Engineer

Finance AI Lead

Senior Applied AI Engineer – Finance

Senior Applied AI Engineer – Finance

AI Engineer – III

AI Engineer

Senior Software Engineer – AI Engineering, Productivity

Machine Learning, AI Developer

Senior AI Engineer, Agentic Evaluation – V&V

AI Application Developer