AI Engineer

• 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, performing data preprocessing, feature engineering, and model training while ensuring HIPAA and regulatory compliance
• 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 updated 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

Principal AI Engineer

• Assess current AI usage, engineering workflows, skill levels, pain points, and opportunities across the team
• Identify highest-value use cases for AI-assisted development
• Evaluate AI tools and practices and recommend where to standardize, experiment, or avoid adoption
• Develop an AI engineering enablement upskill strategy and prioritized roadmap
• Establish repeatable practices, standards, playbooks, workflows, or reusable tooling across project teams
• Upskill developers and Staff Engineers through hands-on application of AI in real development workflows
• Ensure AI-assisted development maintains standards for architecture, maintainability, testing, code review, security, privacy, and quality
• Establish baselines and measures for whether AI adoption improves software delivery profit margin
• Help SmartLogic build software faster and better through responsible AI-assisted development
• Account for client IP, confidentiality obligations, sensitive health information, and varying AI permissions across client projects

AI Engineering Enablement & Adoption Coach

• Lead day-to-day delivery of complex AI engagements across strategy, product, technology, and transformation
• Connect AI initiatives to business cases, roadmaps, capabilities, operating changes, and adoption
• Help clients identify where AI can create meaningful value and determine what needs to change for solutions to succeed at scale
• Translate lessons from products and use cases into broader organizational implications
• Translate business priorities and user problems into AI opportunities
• Lead product discovery with client teams and end users
• Shape AI products and solutions from concept through prototype, build, deployment, and adoption
• Define priorities, success measures, and the optimal path to value
• Lead AI engineers, designers, and client teams across business, user, and technical needs
• Prototype and test ideas using modern AI tools, agents, workflows, and automations
• Own delivery across multiple workstreams, structure work, set priorities, and solve problems proactively
• Build trusted relationships with senior client stakeholders through workshops and challenging assumptions
• Take increasing ownership of client relationships, proposals, and commercial opportunities

Applied AI Lead

• Execute functional bring-up and system integration on subsystem and system benches
• Evaluate system interactions across compute platforms, ECUs, sensors, and vehicle software
• Execute functional, performance, and validation tests using simulation, HIL, SIL, benches, and on-road fleets
• Develop Python-based automation and supporting tools to improve testing efficiency and accelerate development cycles
• Analyze test data, document results, consolidate findings, and support root-cause investigations
• Support creation of datasets, scenario libraries, and automated test pipelines for HIL and SIL environments
• Identify problem statements, outline effective solutions, and account for tradeoffs and edge cases
• Participate in technical discussions, test planning, design reviews, and cross-functional problem-solving
• Collaborate with software, hardware, systems, safety, and vehicle engineering teams to ensure seamless system integration
• Communicate test results, technical findings, and recommendations clearly across multiple stakeholders

Summer Intern – Software Verification Engineer, AV/AI Platform

• Design and implement retrieval-augmented generation and tool-calling flows
• Deliver robust JSON and schema-bound outputs with validation, retries, and fallbacks
• Integrate function calling with internal tools, search, routing, and data services
• Ship APIs and workers in Python or TypeScript with clear contracts, streaming, and backoff
• Add caching, request shaping, prompt templates, and context packing to control latency and cost
• Integrate with AWS Bedrock, OpenAI, Anthropic, or self-hosted endpoints
• Collaborate on chunking, embeddings, and indexing for documents, time series, and multimedia
• Choose and tune vector backends such as OpenSearch, pgvector, or Pinecone
• Maintain knowledge bases through data synchronization from S3, Aurora, DynamoDB, and external sources
• Create offline evaluations and golden sets for prompts, retrievers, and tools
• Establish online metrics for task success, hallucination rate, retrieval precision/recall, p95 latency, and cost per request
• Run A/B tests and prompt/version rollouts with guardrails and canaries
• Implement content and policy checks, PII detection and redaction, access controls, and auditing
• Design human-in-the-loop paths for sensitive actions
• Add tracing, logs, and dashboards for model calls, token usage, errors, and saturation
• Debug failures across retrieval, prompts, tools, and providers
• Partner with product, infrastructure, ML, and security teammates to deliver reliable aviation systems
• Build user-facing LLM features for Beacon AI’s platform that makes flying safer, more efficient, and more capable

Senior Software Engineer – Artificial Intelligence/LLM

• Build user-facing LLM-powered product features end-to-end
• Design and implement retrieval-augmented generation and tool-calling flows
• Deliver robust JSON and schema-bound outputs with validation, retries, and fallbacks
• Add function calling to integrate internal tools, search, routing, and data services
• Ship APIs and workers in Python or TypeScript with clear contracts, streaming, and backoff
• Add caching, request shaping, prompt templates, and context packing to control latency and cost
• Integrate with AWS Bedrock, OpenAI, Anthropic, or self-hosted endpoints
• Collaborate with infrastructure teammates on chunking, embeddings, and indexing for documents, time series, and multimedia
• Choose and tune vector backends such as OpenSearch, pgvector, or Pinecone
• Maintain fresh knowledge bases through data synchronization from S3, Aurora, DynamoDB, and external sources
• Create offline evaluations and golden sets for prompts, retrievers, and tools
• Establish online metrics for task success, hallucination rate, retrieval precision/recall, p95 latency, and cost per request
• Run A/B tests and prompt/version rollouts with guardrails and canaries
• Implement content and policy checks, PII detection and redaction, access controls, and auditing
• Design human-in-the-loop paths for sensitive actions
• Handle aviation data according to internal security standards
• Add tracing, logs, and dashboards for model calls, token usage, errors, and saturation
• Debug failures across retrieval, prompts, tools, and providers
• Set technical direction across services and teams for ambiguous, cross-team problems
• Partner with ML/ infrastructure and product teammates on user experience, outcomes, reliability, and safety-critical systems

Staff Software Engineer, Artificial Intelligence/LLM

• Implement the ServiceNow Compliance roadmap
• Partner with Compliance, Internal Audit, Cybersecurity, and Business stakeholders
• Ensure regulatory and audit requirements are supported through ServiceNow solutions
• Increase workflow automation adoption in and off the platform
• Define the enterprise AI roadmap and governance within ServiceNow
• Drive adoption of Now Assist, Generative AI, Predictive Intelligence, AI Search, Virtual Agent, AI Agents, and AI Control Tower
• Identify high-value AI use cases and build business cases
• Ensure responsible and compliant use of AI technologies
• Serve as a strategic bridge between Security, Risk & Compliance organizations and Platform Transformation teams
• Drive platform value realization and adoption of modern ServiceNow capabilities while maintaining alignment with enterprise architecture and security standards

ServiceNow Compliance – AI Lead

• Own end-to-end technical architecture and delivery of a core software platform, from prototype through production and long-term enhancement
• Build production LLM and agentic applications, including retrieval over project and construction data, agent and tool orchestration, evaluation harnesses, context engineering, and human-in-the-loop review paths
• Integrate AI solutions with enterprise systems of record, including Procore, Salesforce, Bluebeam, ERP and finance systems, and Microsoft Fabric
• Establish engineering standards across the AI team, including repository structure, testing and model evaluation, release process, observability, consumption and cost monitoring, and security review
• Serve as senior technical owner of Blattner’s core AI platform and remain accountable for adoption and operating costs
• Partner closely with a Senior AI Product Manager
• Provide technical direction to AI Engineers, Data Engineers, and Full Stack Application Developers

Lead AI Engineer

• Develop scalable infrastructure and tools supporting model training, model deployment to edge devices, egression, rules-based models, operations, and inference
• Develop and optimize the AI training platform
• Improve data processing pipelines
• Accelerate model training and inference workflows
• Enhance testing infrastructure
• Support tool development, instrumentation, and system monitoring to boost reliability, reduce latency, and increase iteration speed
• Suggest, collect, and synthesize requirements with managers or tech leads to create feature roadmaps
• Code deliverables in tandem with the engineering team
• Adapt standard machine learning methods to exploit distributed clusters, multicore SMP, and GPU environments
• Perform team-specific responsibilities

Summer Intern – Machine Learning Engineer, AV/AI Platform

• Develop, test, and deploy production-ready code across vehicle platform software components
• Quickly ramp up on assigned codebase, product area, and/or system
• Complete assigned tasks efficiently with few iterations
• Identify problem statements and outline optimal solutions while accounting for tradeoffs and edge cases
• Participate in design reviews, code reviews, technical discussions, and continuous integration and deployment practices
• Collaborate with hardware, systems, test, and safety teams to ensure seamless software integration
• Communicate effectively across multiple stakeholders
• Contribute to software powering self-driving systems and autonomous vehicles
• Work with platform operating systems, drivers, embedded MCU software, core services, tools, vehicle software integration, and system or ML-workload performance depending on team needs

AI Engineer jobs in United States