AI Engineer

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs
• 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

Machine Learning Engineer

• Build statistical models and production systems to balance advertiser performance with business goals.
• Tune optimization parameters, measure internal competition, and model dynamic environments.
• Design and run experiments to validate theories underpinning the mobile ad tech economy.
• Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations.
• Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM).
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

• Design, build, and deploy machine learning models—including generative AI models—to address complex business problems.
• Work with teammates to preprocess, clean, and analyze structured and unstructured data for use in ML pipelines.
• Tune and optimize models for accuracy, efficiency, and scalability in production environments.
• Collaborate with MLOps Engineers to ensure smooth deployment, monitoring, and maintenance of ML models.
• Stay informed on the latest advancements in machine learning, deep learning, and generative AI technologies, and apply them where appropriate.
• Contribute to a collaborative engineering culture by sharing knowledge, participating in code reviews, and supporting team initiatives.

• Stand up and operate BetMGM's ML platform on AWS (SageMaker Training, Model Registry, Pipelines, Endpoints, Batch Transform) and Snowflake (Snowpark ML, Cortex), with Terraform-managed infrastructure.
• Build self-service scaffolds that let data scientists ship a model end-to-end without a ticket queue — cookie-cutter project templates with CI, drift monitoring, alerting, IaC, and Snowflake connectivity pre-baked.
• Design and operate batch scoring pipelines — SageMaker Batch Transform, dbt-orchestrated scoring against Snowflake, Snowpark ML — with explicit freshness and cost SLAs.
• Design and operate real-time inference paths — SageMaker real-time endpoints, Lambda + Bedrock for GenAI, API Gateway — with stated latency budgets (typically sub-100ms) and graceful degradation under load.
• Own the feature store (SageMaker Feature Store, Tecton, or Feast) with guaranteed online/offline parity — training-serving skew is treated as an incident, not a tradeoff.
• Build CI/CD for ML — model registry, automated retraining triggers, model versioning, lineage from feature → training run → deployed model → live prediction.
• Implement champion/challenger, shadow deployments, and canary releases as platform primitives so individual model teams do not reinvent them per project.
• Stand up drift detection, data quality, and model performance monitoring (Evidently, Arize, or SageMaker Model Monitor — pick one and standardize) with paging that routes to humans who can fix it.
• Own MLOps incident response — production model failures are SEV events with postmortems.
• Right-size endpoints, batch caching, request batching, and autoscaling. State cost-per-prediction targets up front and meet them.
• Integrate LLM APIs (Bedrock, Anthropic, OpenAI) into production paths — RAG pipelines, agent eval frameworks, prompt versioning, cost and latency observability.

Senior Machine Learning Operations Engineer

• Lead advanced development efforts and collaborate closely with pipeline engineering and deployment teams
• Architect solutions, tackle sophisticated challenges across Sea-based systems and deliver capabilities that exceed current performance benchmarks through the integration of emerging AI technologies, advanced features, and next-generation methodologies
• Strong collaboration with team’s in the design and implementation of AI architectures to include operational status, coding improvements, dashboard tooling including security
• Supports Leidos AI initiatives, providing insight into technologies, tooling and practices
• Designs, secures and automates AWS cloud initiatives while optimizing agent and customer experiences to full AI potential
• Optimize and enhance customer experiences, enhancing software development flows, quality metrics, KPIs, and Dashboards with AI
• Provides broad-based experience in the systems engineering lifecycle and applies experience/knowledge to cloud initiatives relating to architectural design and development
• Leads team’s to deploy and configure services with AI tools
• Ensures AI models, pipelines, and cloud-deployed services adhere to governance, compliance, and security standards, providing guidance on responsible AI implementation across mission systems

Senior AI Software Engineer

• 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

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• 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 AI solutions and development
• Collaborate with pipeline development and deployment teams
• Optimize and enhance customer experiences with AI
• Deploy and configure services with AI tools
• Design and secure AWS cloud initiatives
• Provide broad-based experience in the systems engineering lifecycle

AI Software Engineer – Lead

• Architect and lead the development of agentic AI systems that automate finance workflows.
• Design and implement multi-agent systems leveraging LLMs and orchestration patterns.
• Translate cutting-edge research into scalable solutions.
• Establish guardrails and responsible AI practices for safe outputs.
• Lead the design of advanced predictive models for customer behavior forecasting.
• Develop interpretable, production-grade models for retention strategies.
• Design scalable AI/ML systems with emphasis on software engineering best practices.
• Establish MLOps best practices for AI systems deployment and governance.
• Set technical direction for AI/ML initiatives across finance.

Principal Applied AI Engineer, Finance

• Design, build, and deploy core application components of EVERSANA’s AI Agency Platform.
• Develop backend services, APIs, and front-end interfaces integrated with third-party systems.
• Collaborate with AI/ML Engineers to integrate model endpoints and services.
• Maintain code quality, testing coverage, and deployment reliability across environments.
• Ensure platform design meets pharma regulatory expectations for traceability and content integrity.

Senior AI Developer, Engineering

AI Research Scientist

• Build, deploy, and maintain production-grade AI and ML pipelines, backend services, and infrastructure that support Oura's AI Advisor and custom model initiatives
• Design and implement evaluation systems and tooling to measure grounding, reliability, and actionability of LLM and agentic systems in production
• Develop and deploy agentic AI systems in production
• Lead initiatives end to end — from problem definition and research through deployment, monitoring, and iteration
• Partner with Product and Engineering teams to translate data-driven insights into shipped product features
• Build and optimize custom AI solutions through fine-tuning, knowledge distillation, and quantization
• Help build Oura as a leader in AI expertise and infrastructure
• Proactively identify opportunities to improve the product and bring your own ideas to the table

Senior AI Scientist

• Design and implement data-driven systems that enhance decision-making across dealer workflows
• Develop predictive or optimization logic using structured dealership data
• Translate business objectives into scalable intelligent services
• Improve consistency, accuracy, and efficiency in operational processes
• Ensure solutions are explainable, reliable, and aligned with enterprise and regulatory expectations
• Build systems that extract insights from structured dealership data
• Implement knowledge retrieval or search capabilities across policies and documentation where appropriate
• Collaborate with product teams to surface actionable intelligence within dashboards and workflows
• Design and deploy containerized AI services within AWS (ECS/Fargate)
• Integrate intelligent services into existing APIs and application workflows
• Monitor system performance, reliability, and integrity
• Ensure data security, auditability, and compliance alignment

AI/ML Engineer – Automotive CRM

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes.
• Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc)
• Monitor the latest ML research for functional ideas that the team could try.
• Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing.
• Model Monitoring & Improvement – Track performance, detect drift, and automate retraining.
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences
• Collaborate with a team of world-class engineers with diverse backgrounds.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

Machine Learning Engineer, Conversion ML

• Design and implement agentic AI systems capable of multi‑step reasoning, tool use, and autonomous task execution.
• Architect enterprise‑grade GenAI solutions, including RAG pipelines, embedding strategies, orchestration layers, and evaluation frameworks.
• Lead PoCs and pilots and scale successful solutions into production‑ready capabilities.
• Establish reusable architectural patterns, reference implementations, and technical standards for GenAI and agentic systems.
• Integrate AI solutions with existing platforms, data sources, and workflows to support adoption.
• Mentor team developers and contribute to raising overall GenAI and agentic AI maturity.
• Embed governance, security, evaluation, and monitoring considerations into solution design and deployment.

PharmSci Agentic and Generative AI Architect

• Lead and scale the Forward Deployed AI Engineering team across its two functions: customer-embedded engineering and AI & Data Science
• Own the team's mandate: embed in strategic customer accounts to discover problems, build high-grade solutions, and produce cross-customer intelligence that elevates the entire platform
• Define the engagement model for FDE work — how engineers are assigned to accounts, how engagements are scoped, and how solutions are documented and productized
• Oversee the AI & Data Science function — ensuring cross-customer telemetry, threat intelligence, and detection optimization work is translated into tools that the SE&A team can operate
• Partner with Sales to identify strategic accounts that are candidates for FDE engagement
• Partner with the SE&A and Rapid Deployment teams to ensure FDE-built solutions flow back into the broader delivery model
• Serve as an executive-level presence with strategic customers — participating in account reviews and helping define the roadmap for embedded engagements
• Evaluate new AI technologies, data science tooling, and engineering approaches that can improve team output and customer outcomes
• Hire, develop, and retain engineers across both the FDE and AI & DS functions

Director of Forward Deployed AI Engineering

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