Machine Learning Engineer

• Leading a small team consisting of developers and researchers to implement AI/ML algorithms to solve computer vision problems in remote sensing
• Analyzing and pre-processing large remote sensing datasets
• Researching supervised and unsupervised ATR solutions for remote sensing
• Researching the performance benefits of multimodal data fusion for ATR, under diverse data collection scenarios
• Training, optimizing, and deploying deep learning CV models that provide analysts with actionable insights
• Reviewing relevant publications to understand cutting edge concepts, break down key ideas to government customers, and apply to defense and commercial applications
• Writing technical documentation supporting code, program capabilities and user-guides

Staff Computer Vision AI/ML Engineer

AI Engineer

• 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

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

• Build and ship production-grade AI systems used daily by real customers
• Design multimodal pipelines for drawings, specs, and construction documents
• Build agentic workflows automating preconstruction tasks: Document intake, Extraction, Takeoff support, Bid preparation, QA workflows
• Build and improve: Retrieval systems (embeddings, reranking, chunking), Evaluation systems, Orchestration layers
• Optimize model quality, latency, reliability, and cost efficiency
• Prototype and iterate quickly on customer-facing AI features
• Own systems end-to-end (experiment → deploy → monitor → improve)
• Help shape engineering culture and long-term technical direction

Founding AI/ML Engineer

• Provides leadership in hiring, coaching, talent development, performance management, and compensation decisions
• Partners with Enterprise, Solution, and Domain Architects to define AI/ML solution architectures
• Leads cross-functional delivery across Product, Data Science, Platform, and Engineering teams
• Establishes engineering practices, reusable frameworks, and platform capabilities for AI/ML initiatives
• Oversees the design, implementation, and evolution of data, feature, and model pipelines
• Applies expertise in machine learning, statistics, and optimization methodologies
• Evaluates data quality and feature readiness in partnership with Data Science teams
• Drives operational excellence through automation and continuous improvement practices
• Engages business and technology stakeholders to understand objectives and assess opportunities
• Communicates technical strategy and delivery progress to senior leaders
• Participates in special projects and performs other duties as assigned

Manager, Machine Learning Engineer

• Lead the operational architecture, deployment strategy, and reliability engineering for integrating AI into high-stakes Healthcare Information Systems (HIS)
• Define the enterprise operational standards, govern the release processes, and build the resilient infrastructure required to maintain models in mission-critical clinical environments
• Architect and govern the comprehensive release process, defining enterprise checklists, automated approval gates, release notes, and deployment readiness standards
• Establish the deployment execution standards for promoting AI across all environments and ensure customer deployments adhere to strict internal production discipline
• Architect and oversee the enterprise model registry, ensuring seamless integration with CI/CD pipelines and full version control traceability
• Define and enforce monitoring standards, establishing critical SLAs/SLOs, service health metrics, and comprehensive dashboards across the AI ecosystem
• Architect automated checks for input/output data quality and model drift, ensuring proactive detection of system degradation
• Establish and lead the production incident process, including rigorous triage workflows, severity escalation paths, postmortems, rollback mechanisms, and recovery infrastructure
• Partner with Platform teams to provide essential ATO (Authority to Operate) and compliance support, ensuring complete deployment traceability and strict operational controls
• Oversee comprehensive operational reporting, providing leadership with status updates across production systems, pre-prod testing, customer rollouts, and incident metrics
• Foster a culture of production discipline, guiding junior engineers in maintaining operational runbooks and reliable deployment pipelines

Principal MLOps Engineer

• Develop spatio-temporal machine learning models for multi-object tracking and uncertainty estimation.
• Develop machine learning models to estimate road features, lane markings, road shape and road topology
• Read literature, analyze raw data, and design state-of-the-art solutions
• Transition solutions from the lab to the test track and public roads to ensure successful production-level implementation
• Collaborate with perception experts and experienced roboticist on algorithm design, prototyping, testing, deployment, and productization
• Build and maintain industry-leading software practices and principles
• Develop clean and efficient software for perception modules interfacing with other key modules
• Show initiative and be a valued team member in a fast-paced, innovative, and entrepreneurial environment

Senior Software Engineer – Machine Learning, Multi-Object Tracking

• 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

Senior Software Engineer – AI Engineer

• 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

Chief AI Architect

• Designing and implementing AI and machine learning solutions to transform raw data into actionable insights
• Developing scalable AI systems using advanced algorithms and software engineering techniques
• Collaborating with clients to understand their needs and deliver tailored data solutions
• Utilizing data wrangling and integration skills to prepare data for AI model deployment
• Applying machine learning libraries such as TensorFlow and Scikit-Learn to enhance model performance
• Conducting complex data analysis to inform decision-making and drive business growth
• Building and maintaining data pipelines and infrastructure to support AI initiatives
• Leveraging programming languages like Python and Java for AI system development
• Engaging in continuous learning to stay updated with the latest AI and machine learning advancements
• Mentoring junior team members and guiding them in AI project execution

Senior Associate – Risk Architecture, Agentic AI, Machine Learning Developer

• Apply cutting-edge AI/ML-based Computer Vision algorithms to problems in remote sensing
• Responsible for maturing, optimizing, and deploying algorithms to a cloud-hosted environment
• Serve as a technical leader and guide a team of researchers and developers

• Lead a small team to implement AI/ML algorithms for computer vision problems
• Analyze and pre-process large remote sensing datasets
• Research supervised and unsupervised ATR solutions
• Train, optimize, and deploy deep learning CV models
• Write technical documentation supporting code and user-guides

Senior Computer Vision AI/ML Engineer

• Join the Engineering team as a Forward Deployed AI Engineer
• Act as a resident AI expert within Individual Departments/Business Units
• Function as engineer, solutions architect, and internal consultant
• Design and build AI-powered application features using LLM APIs
• Create agent loops that can select tools, execute actions, and summarize results
• Develop chat-based analytical experiences connecting user questions to backend tools
• Improve prompt quality and manage context windows
• Use advanced AI coding tools to accelerate development while maintaining code quality
• Embed directly with internal departments to identify where AI can drive efficiency
• Lead the end-to-end implementation of AI solutions within operational contexts
• Provide hands-on troubleshooting and support for AI models running in production mode

AI Engineer, Forward Deployed

• Engage with the Digital and Innovation (D&I) leaders to promote the D&I vision
• Assume responsibility for the success of the enterprise data platform, data engineering delivery, and AI architecture ensuring Westinghouse digital solutions
• Lead the data engineering and AI architecture team in establishing the data architecture and data fabric
• Establish integration patterns and platform services to promote the use of industry data across multiple use cases
• Mentor team members responsible for cloud architecture and infrastructure; establish best practices for cloud platforms, security, availability, cost management, and operational support according to Westinghouse Information Technology
• Develop team member critical skills such as Data architecture, data modeling, and governance Data ingestion, transformation, and quality (ETL/ELT) Data integration, master data, and data interoperability

Manager, AI Architecture

• Design, develop, and fine-tune a variety of AI models
• Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration; employ the Model Context protocol to manage stateful interactions
• 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

• Design, build, test, and deploy custom agents, plugins, and connectors in Microsoft Copilot Studio and Azure AI Foundry
• Implement retrieval, grounding, and indexing patterns against SharePoint, Microsoft Graph
• Prototype and productionize agents that automate high-value crew workflows
• Lead proof-of-concept work for new Microsoft AI capabilities
• Partner with Workplace AI Engineer on agent governance

AI Engineer – Workplace AI

• Designing, developing, and deploying generative AI models (e.g., LLMs, diffusion models)
• Architecting and building agentic systems with autonomous decision-making capabilities
• Integrating generative AI and agentic solutions into existing products and services
• Developing and maintaining the software infrastructure required for AI/ML pipeline
• Developing and implementing data pipelines for training and evaluating AI models
• Conducting research and experimentation with new generative AI and agentic techniques
• Other duties as assigned or requested

See all jobs on JobTailor

Staff Computer Vision AI/ML Engineer

SAP Datasphere, SAC, AI Architect

Senior AI Engineer

Senior Machine Learning Engineer II, Search & Recommendations Ranking

Founding AI/ML Engineer

Manager, Machine Learning Engineer

Principal MLOps Engineer

Senior Software Engineer – Machine Learning, Multi-Object Tracking

Senior Software Engineer – AI Engineer

Technology Delivery Manager – AI Platforms

Lead AI Engineer

Chief AI Architect

Senior Associate – Risk Architecture, Agentic AI, Machine Learning Developer

Staff Computer Vision AI/ML Engineer

Senior Computer Vision AI/ML Engineer

AI Engineer, Forward Deployed

Manager, AI Architecture

AI Engineer – Mission Innovation Lab

AI Engineer – Workplace AI

AI Engineer