AI Engineer

• Partner with business and technology teams to identify, evaluate, and prioritize AI use cases.
• Translate business needs into scalable AI solution designs and implementation plans.
• Lead delivery of AI-enabled products and solutions from concept through production.
• Provide technical leadership to engineers, contractors, and delivery teams.
• Guide architecture, design, and implementation decisions related to AI and software engineering.
• Present AI capabilities, demonstrations, and solution recommendations to business and technical stakeholders.
• Collaborate with enterprise AI, architecture, and technology organizations to align initiatives and leverage existing capabilities.
• Influence AI strategy, adoption, and best practices across WCIB.
• Support team growth through mentoring, leadership, and participation in hiring activities.

AI/ML Enablement Lead Engineer

• Lead the architecture and delivery of AI-powered solutions for law firms and corporate legal departments
• Review and guide system design to ensure quality, scalability, security, and maintainability
• Build sophisticated legal AI solutions tailored to legal use cases
• Drive AI-native engineering practices across the team
• Establish observability and trust frameworks for production AI systems
• Lead technical discovery with clients and stakeholders
• Mentor engineers and partner with experts to advance reusable solution patterns

Staff Software Engineer – AI, CoCounsel FDE

Machine Learning Engineer

• Research, design, develop, and evaluate signal processing, image processing, and computational algorithms for medical device applications
• Assess technical feasibility, performance, and design tradeoffs of new concepts
• Implement complex real-time or near-real-time algorithms in software
• Analyze bench, preclinical, and clinical data to develop/train algorithms, verify and validate them, and guide technical decision making
• Collaborate with cross-functional partners across engineering, design, clinical, and research teams to translate unmet needs into solutions
• Provide technical leadership and mentorship in signal processing and algorithm development

Principal Algorithm Engineer – Imaging, Ultrasound, ML

• Guide technical discovery and scoping legal use cases in client engagements
• Translate legal workflows and client goals into solution designs
• Design and implement AI-powered solutions for legal workflows
• Lead LLM evaluation efforts for legal use cases
• Ensure deployed solutions meet ethical AI and data protection standards

Senior Legal AI Engineer

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• 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 deliver AI-powered solutions across a range of engagement types — from sustained strategic initiatives requiring deep discovery and architecture work, to rapid field engagements focused on integrating LOB workflows with enterprise AI platforms and validating feasibility through working prototypes.
• Leverage enterprise AI capabilities include large language models, agentic workflows, cloud computing services (AWS/Azure), and container orchestration (Kubernetes) to solve complex, real-world business problems with a high degree of accuracy and speed.
• Lead application and solution design in partnership with architects, product owners, and LOB technical leads — translating ambiguous business problems into technically sound, implementable solutions with clear production intent from kickoff.
• Own delivery end to end — from scoping and design through development, handoff, and knowledge transfer — maintaining high standards for code quality and solution design at every stage.
• Actively engage with LOB stakeholders to understand not just the problem at hand, but adjacent challenges and emerging needs — surfacing field intelligence that informs the broader AI product roadmap and strategic investment decisions.
• Provide technical guidance and coaching to junior engineers on the team, serving as an informal technical lead within your team — setting the bar for code quality, solution design, and delivery standards without carrying formal management responsibility.
• Demonstrate subject matter expertise across multiple technology disciplines, serving as a credible technical resource for both LOB partners and internal engineering teams.
• Perform analysis, assessment and resolution for defects and incidents of advanced complexity and escalate appropriately.

Software Engineer II – Enterprise AI Products

• help design, build, deploy, and maintain AI/ML applications that support automation, insight, and action across core business workflows
• work closely with Data Scientists, engineers, product partners, platform teams, security teams, and business stakeholders
• provide hands-on technical leadership for AI engineering initiatives
• contribute to architecture and design decisions
• evaluate appropriate models, frameworks, and tools
• write maintainable production-quality code
• establish strong engineering practices across development, testing, deployment, observability, documentation, and ongoing support
• ensure AI applications are secure, reliable, maintainable, and aligned to Target’s enterprise standards for infrastructure, platform architecture, data handling, and operational readiness
• partner with senior engineers and engineering leaders to shape technical approaches, identify implementation risks, resolve roadblocks, and support the evolution of reusable AI engineering patterns
• stay current with developments in AI, machine learning, LLMs, agentic systems, and modern software engineering practices

Lead AI Engineer – Advanced AI, Applied ML, LLMs, Agentic AI, ML Ops

• Define and execute the vision, strategy, and roadmap for AI, automation, and intelligent workflows across HR
• Identify opportunities to improve employee experiences, decision-making, and operational effectiveness through AI-enabled solutions
• Assess current-state capabilities, design future-state experiences, and develop strategies that accelerate transformation
• Translate enterprise priorities into scalable products, platforms, and capabilities that create value across HR
• Drive the development of reusable AI, automation, orchestration, and workflow capabilities that support enterprise-wide adoption
• Establish product strategies that leverage vendor platforms while identifying opportunities for differentiated, custom-built solutions
• Evaluate emerging technologies, AI platforms, automation tools, and data capabilities to determine optimal solutions and investments
• Own product vision, roadmap prioritization, business cases, and value realization, delivering measurable business outcomes
• Partner across HR, Technology, Engineering, Architecture, and Data Science to deliver innovative, user-centric solutions and drive adoption
• Establish governance, operating models, success metrics, and responsible AI practices that balance innovation, risk, compliance, and sustainable growth

Senior Lead Product Manager – Agentic AI, Automation

• 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

• Responsible for designing, developing, testing, operating and maintaining Enterprise AI Agents
• Takes full stack ownership by consistently writing production-ready and testable code
• Consistently creates optimal design adhering to architectural best practices; considers scalability, reliability and performance of systems/contexts affected when defining technical designs
• Performs analysis on failures, propose design changes, and encourage operational improvements
• Takes feedback from code review and apply changes to meet standards
• Conducts code reviews to provide guidance on engineering best practices and compliance with development procedures
• Accountable for ensuring all aspects of product development follow compliance and security best practices
• Embraces emerging technology opportunities and contributes to the best practices in support of the bank’s technology transformation
• Contributes to a culture of innovation, collaboration and continuous improvement
• Reviews tasks critically and ensures they are appropriately prioritized and sized for incremental delivery; anticipates and communicates blockers and delays before they require escalation

Lead Software Engineer – AI Tools

• 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

• Design, build and scale AI infrastructure and tooling that empowers data scientists, engineers and product teams across Shipt
• Develop internal tooling and libraries using AI tools and frameworks
• Create integrations with LLM gateways, agent frameworks, and structured output systems to improve AI-powered applications
• Contribute to the design of scalable infrastructure for large-scale data processing and model serving
• Partner closely with engineers and data scientists and collaborate with product and operations teams to delivery AI capabilities that power real-world logistics, retail and customer experiences

Senior AI Engineer

AI Research Scientist

• Manage a team of Applied Scientists and Research Engineers, ensuring a high-performance culture.
• Build, mentor, and grow a high-performing team of applied scientists.
• Translate state-of-the-art AI research into scalable, production-ready solutions.
• Partner with Product and Engineering leaders to define and execute the AI-driven product roadmap.
• Oversee scaling of experiment and LLMOps platforms, balancing reusability and innovation.
• Explore emerging technologies, foster experimentation, and share impact and ideas to both technical and non-technical audiences.

Manager, AI and Applied Research

• Lead a cross-functional product pod of Solution Consultants, Pega Configurators, and Business Analysts to deliver Pega-based workflow automation solutions aligned to business priorities.
• Provide day-to-day product direction, delivery oversight, mentorship, and issue resolution support to help the team meet commitments and maintain high execution standards.
• Own solution delivery from discovery and design through configuration, testing, deployment, and post-implementation feedback, with a focus on quality, usability, scalability, and control readiness.
• Work hands-on within Pega App Studio and Dev Studio to support solution design, configuration, testing, and implementation of workflow capabilities.
• Partner with architecture and engineering teams to ensure solutions align with platform architecture, technical standards, security requirements, and software delivery lifecycle expectations.
• Translate operational pain points, process inefficiencies, and business needs into clear product requirements, workflow designs, user stories, acceptance criteria, and delivery plans.
• Partner directly with operational leaders and business stakeholders to assess opportunities, shape solution options, manage expectations, and drive alignment on scope, priorities, benefits, and delivery timelines.
• Establish and monitor success measures for delivered solutions, including adoption, operational efficiency, user experience, quality, risk reduction, and other relevant business outcomes.
• Elevate product management practices across the team by improving intake, prioritization, roadmap planning, stakeholder communications, and outcome measurement.
• Identify and help resolve risks, issues, impediments, and dependencies that could impact delivery quality, control effectiveness, or business outcomes.
• Foster a collaborative, inclusive, and accountable team environment that supports talent development, clear communication, continuous improvement, and operational excellence.

Pega Product Delivery Lead – AI Case Management

• Identify high-impact opportunities to apply AI automation across CIO technology teams.
• Design end-to-end AI solutions including workflows and operational considerations.
• Guide implementation from prototype to production ensuring solutions meet compliance expectations.
• Define reusable architectural patterns for broader adoption across teams.
• Build scalable pipelines to analyze workflow-level telemetry.
• Develop dashboards and reports for visibility into performance.

Lead AI Forward Engineer

• Collaborate with AI platform, product, and engineering teams to evaluate the security posture of AI systems throughout their lifecycle
• Assess AI architectures, models, agents, and supporting infrastructure to identify security risks, vulnerabilities, and design weaknesses
• Develop and execute security validation strategies for AI systems, including threat modeling, attack simulation, and adversarial testing
• Evaluate the effectiveness of existing security controls, guardrails, and mitigations protecting AI applications and platforms
• Identify emerging AI-specific threats, attack techniques, and vulnerabilities, and communicate their potential business impact
• Recommend risk mitigation strategies and prioritized remediation plans to improve the security and resilience of AI systems
• Conduct deep technical reviews of AI products, platforms, and architectures to identify opportunities for security improvement
• Partner with engineering teams to validate secure deployment patterns for AI workloads across cloud and hybrid environments
• Define security assessment methodologies, testing frameworks, and assurance standards for AI technologies
• Provide expert guidance on AI security best practices, including model security, prompt injection defenses, agent security, supply chain security, and data protection
• Prioritize high-impact security improvements that measurably reduce risk while enabling innovation and business objectives
• Perform hands-on security analysis and testing of complex AI-enabled systems, identifying gaps in architecture, implementation, and operational controls
• Collaborate with security, architecture, and engineering teams to continuously improve AI security controls and governance practices
• Efficiently assess and communicate security risks to stakeholders, balancing technical realities, business priorities, and organizational objectives
• Serve as a trusted advisor on AI security, helping teams make informed decisions as AI capabilities evolve across the enterprise

Principal Engineer – Secure AI

• 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

• Contribute to the design of AI-enabled solution architectures within the Avanade Catalyst Solutions portfolio
• Apply and help develop reusable patterns, reference architectures, and accelerators
• Adapt pre-built AI solutions to support client needs and evolving market requirements
• Ensure solutions meet standards for scalability, security, and production readiness
• Collaborate with engineering and delivery teams to identify opportunities for AI-driven tools and methods
• Support the development of repeatable, outcome-based services
• Help drive consistency in how AI solutions are implemented across engagements
• Support architecture and deployment of ready-made AI offerings
• Partner with security, risk, and compliance teams to align with enterprise requirements
• Provide guidance to delivery teams

Manager, Global AI Enterprise Architect

• Act as an advisor to senior leadership in developing a large cross functional team to identify and execute complex artificial intelligence initiatives that span a large line of business
• Provide consultation to more experienced leaders in order to recommend solutions which solve business challenges
• Lead the strategy and resolution of highly complex challenges ensuring the solution delivers the intended benefits
• Leverage Artificial Intelligence expertise to evaluate technological readiness, data availability, and resources required to execute the proposed solutions
• Provide direction on key issues which may arise during development or implementation
• Cultivate relationships with clients to identify and develop a pipeline of future Artificial Intelligence opportunities which align with the strategic priorities of the business
• Collaborate and consult with peers, colleagues, and managers to resolve issues and achieve goals

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