AI Engineer

• Build high-performance API services and implement complex RAG and Agentic AI architectures.
• Design and implement end-to-end RAG pipelines, including retrievers, vector stores (e.g., Pinecone, Weaviate, or pgvector), and generators.
• Optimize latency and relevance to ensure production-grade performance.
• Develop autonomous or semi-autonomous agents using frameworks such as LangChain, CrewAI, or Semantic Kernel.
• Manage orchestration, tool integration, and robust error handling for non-deterministic AI outputs.
• Assess performance, grounding accuracy, and hallucination detection using evaluation frameworks (e.g., RAGAS, TruLens).

Gen AI Engineer

• Establishes the architectural patterns, evaluation practices, and deployment standards for the team
• Makes framework and model recommendations that set the foundation for the team
• Collaborates with the AI Enablement Manager and business unit to optimize operational processes, owning the infrastructure architecture and translating strategic direction into technical plans, product life cycles, a prioritized backlog, and buildable work
• Owns solutions end-to-end: technically planning, architecture, build, and deployment, then continuously monitors, iterates, and improves them to keep them reliable in production
• Partners with data governance to ensure solutions meet compliance, data quality, and operational standards
• Builds and continuously maintains the monitoring, evaluation, and regression detection systems that keep production agents reliable over time, including logging, performance benchmarking, and feedback loops that surface drift early.
• Leads the team as it grows, onboarding new team members like AI Product Manager and AI Operations Specialists for intake, sequencing, stakeholder coordination, and product ownership of deployed solutions.

Lead AI Engineer, Business Operations

Machine Learning Engineer

• Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI.
• Scale performance of DL models across different architectures and types of NVIDIA accelerators.
• Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions.
• Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions.

Deep Learning Software Engineer, Inference

• Define and evolve the standards, architecture patterns, and tooling choices that govern how engineers use AI across the codebase — including where to invest in codebase readiness (instrumentation, specification, evaluation) versus targeted rewrites.
• Own execution of the multi-milestone plan (readiness → validated → governed) applied to corgi and secondary repos, in partnership with squad leads — without being their manager.
• Decide what AI tooling, agent infrastructure, and evaluation pipelines the org needs, and build or oversee the build of the ones that don't exist yet.
• Set the policy and technical controls for where human-in-the-loop review is required versus where autonomous agent action (PR review, deployment) is safe — resolving the tension between adoption speed and security/quality risk.
• Write the guidance engineers actually use — AGENTS.md/CLAUDE.md conventions, review norms for AI-generated code, eval standards — and make sure it's adopted, not just published.

AI Architect

• Contribute in all areas of development as a member of an Agile Scrum team
• Demonstrate excellence in the software development practices performed by Scrum teams
• Develop product features for any or all of the following: data marketplaces, mobile applications, web applications, and API services
• Use automated pipelines for building, testing, and deploying software packages
• Maintain a production-first mindset focused on scale, uptime, and ability to rapidly triage/resolve production issues

Software Development Engineer II – Gen AI, Innovation

• Lead Agentic Strategy: Design and implement voice and digital AI Self-Service solutions for large contact centers using Gemini Enterprise CX and Agent Development Kit (ADK).
• Architect Autonomous Workflows: Design multi-agent orchestrations that leverage Reasoning and Acting (ReAct) frameworks to meet complex client needs.
• Technical Leadership: Serve as the lead architect for Google GECX, Gemini Enterprise and Agent Development Kit (ADK) projects, CI/CD, driving technical design and solution requirements. Translate high-level architecture into technical implementation designs.
• Integration & Tool Use: Develop seamless integrations between AI agents and backend systems using REST/SOAP APIs, ensuring agents can perform real-time data retrieval and execution. Build and implement using: Gemini AI, Cloud Run/GKE, Cloud Functions, API Gateway, Pub/Sub, Dataflow and IAM.
• Advanced Conversation Design: Collaborate with teams to move from rigid decision trees to fluid, LLM-driven conversation specifications.
• Testing & Optimization: Lead unit, system, and functional testing of agentic code, focusing on prompt engineering and hallucination mitigation.

Agentic AI Architect – Google Gemini Enterprise CX

• Use AI tools and agents to accelerate test creation, execution, and triage across Zello's clients and services
• Do targeted manual testing where it matters, and turn repetitive manual checks into durable automation
• Build small tools and AI harnesses that make the whole engineering org faster
• Rotate into engineering squads (iOS, Android, Web, backend) to contribute code with agent assistance
• Find quality gaps before they ship, and propose the fix rather than just filing the bug
• Communicate clearly and concisely with a distributed team, surfacing blockers early

Software Engineer I – AI Generalist

• Analyzing client needs to implement and integrate business applications effectively
• Providing training and support to clients for seamless utilization of software solutions
• Leveraging analytical reasoning to optimize operational efficiency through business applications
• Collaborating with clients to understand and address their strategic objectives
• Utilizing business data analytics to inform insights and recommendations
• Managing project delivery and resource management to meet client expectations
• Implementing technology solutions to support business transformation initiatives
• Developing documentation to support application software implementation and integration
• Engaging in strategic planning to align technology solutions with business goals
• Navigating complex situations to build meaningful client relationships and inspire team members

AI Integration Developer, Senior Associate

• Build production AI platform capabilities that transform Prologis building, project, asset, and operational data into reusable solutions for construction, procurement, and operations teams.
• Design and implement production AI platform capabilities, including agents, retrieval-augmented generation, tool calling, workflow orchestration, evaluation, and human review.
• Build an AI-native building knowledge platform that links building models, assets, documents, telemetry, and operational context.
• Develop multimodal data pipelines that convert BIM, Autodesk, geospatial, document, and operational data into structured, queryable knowledge.
• Deliver AI workflows supporting design, construction, commissioning, building operations, sustainment, and reinvestment use cases.
• Move priority solutions from prototype through pilot and production with appropriate governance, documentation, evaluation, observability, and operational readiness.
• Establish reusable engineering patterns that enable successful pilots to scale into enterprise platform capabilities.
• Partner with business, technology, security, and operations teams to define workflows, technical scope, implementation plans, rollout approaches, and post-launch improvements.

Lead AI Engineer

• Architect, design and develop RESTful API Endpoints, ensuring high performance, scalability and maintainability.
• Collaborate closely with front-end engineers to build dynamic, responsive, and user-centric web applications that deliver seamless user experiences.
• Define robust data models and database schemas, and manage relational or NoSQL databases with a strong emphasis on data integrity and performance optimization.
• Direct and govern AI coding agents across the delivery lifecycle, setting context, reviewing agent-generated output, and enforcing SEPS delivery standards at every gate.
• Design and maintain agent workflows using agentic coding tools (Claude Code, Cursor, Copilot) and the AI-native development framework, ensuring structured, auditable, and high-quality delivery.
• Configure and maintain prompt engineering patterns, structured output schemas, with rules and guardrails to align agent behavior with SEPS platform standards.
• Trace, debug, and triage agent tool calls, prompt failures, and unexpected outputs, maintaining visibility into AI-assisted delivery at every stage.
• Create and maintain clear, comprehensive documentation for application architecture, design, configuration, and ongoing maintenance.
• Provide accurate task estimations and delivery timelines, contributing to effective sprint planning and execution.
• Participate in the on-call rotation, responding promptly to production issues and ensuring system stability and uptime.
• Mentor and support junior engineers, fostering a culture of AI-native ways of working, learning, and continuous improvement.

Staff Software Engineer, AI Native

• You will be building, optimizing, and deploying state-of-the-art agentic AI/ML models and systems within the Verizon Consumer Group (VCG) AI team
• Applying hands-on technical expertise, you will be implementing next-generation customer success, sales, and digital agent solutions that directly impact how millions of customers interact with Verizon
• Working as part of a high-performing squad, you will turn architectural designs into production-grade code while ensuring reliability, performance, and scale
• Developing, testing, and scaling agentic AI/ML solutions, specialized workflows, and LLM-powered systems for VCG platforms
• Integrating AI agent frameworks with existing customer success, digital, and retail channel technologies
• Collaborating with product managers and GTS engineering teams to implement and iterate on production-grade technical roadmaps
• Participating in engineering code reviews, technical design sessions, and red-teaming exercises to ensure safe and reliable model behaviors
• Optimizing model inference pipelines, API endpoints, and data integration layers to meet high-performance consumer demands
• Assisting in technical research and rapid prototyping to quickly evaluate and implement emerging open-source and proprietary AI frameworks
• Contributing to the documentation of technical specifications and creating materials to showcase pilot performance results

Senior AI Engineer

• Lead the technical vision, architectural design, and execution of cutting-edge agentic AI/ML solutions within the Verizon Consumer Group (VCG) AI team
• Shape the future of customer experience by building intelligent, autonomous agents that power digital channels, sales, and customer success ecosystems
• Translate long-term strategy into robust, scalable, and secure production systems with cross-functional leadership
• Drive technical strategy, research, and application of advanced agentic AI, large language models (LLMs), and cognitive frameworks
• Design and implement production-grade, highly scalable AI/ML pipelines and agent architectures
• Collaborate with Global Technology Architecture, Security, and Legal teams to ensure data privacy and compliance
• Partner with Customer Service Operations and VCG Strategy teams to identify high-impact business applications
• Develop and maintain a technical roadmap for agentic systems across VCG
• Mentor engineers and establish engineering best practices

Principal AI Engineer

• use technical expertise in data science and real-time AI systems to lead a team focused on the production lifecycle, health, and optimization of deployed models
• ensure commercial AI applications remain highly accurate, safe, and operationally efficient
• monitor and observe real-world model performance to proactively detect model drift, track latency, and manage computational expenses
• design and implement continuous evaluation loops to assess model accuracy on a day-to-day basis
• manage incident response protocols and automated safety guardrails
• establish fallback architectures to ensure a seamless experience if a model fails
• automate continuous deployment (CI/CD) pipelines
• operate as a technical thought leader and trusted advisor to partner with broader data science teams and senior leadership

Associate Director, AI Engineering – Live Operations

• Apply advanced technical expertise in data science, system orchestration, and software engineering to maintain the stability, safety, and efficiency of deployed AI models
• Monitor production model behavior to identify data drift and model degradation, ensuring high predictive accuracy over time
• Implement automated continuous evaluation loops that capture user feedback to dynamically assess performance
• Deploy real-time safety filters, system guardrails, and input/output moderators to mitigate model hallucinations
• Integrate robust fallback systems to preserve end-user experience
• Develop automated continuous integration and deployment pipelines to retrain, validate, and release updated models
• Resolve production model incidents as a senior escalation contact, diagnosing issues with live models
• Collaborate with platform architects and core data science teams to scale backend architectures across cloud, on-premises, and edge network infrastructures

Senior AI Engineer – Live Operations

AI Research Scientist

• Design and implement end-to-end model pipelines to production across multiple product domains, including ranking, recommendations, search, and personalization
• Develop and maintain scalable data pipelines, data quality checks, and model monitoring to ensure reliability, performance, and responsible behavior of ML systems in production
• Collaborate with cross-functional partners (product, analytics, engineering) to translate ambiguous business needs into well-scoped ML projects, communicate findings, and influence decision making with data-driven insights
• Use A/B tests and offline/online evaluation frameworks to measure model impact and guide iterative improvement

Machine Learning Scientist III – Whole Trip AI

• Design, build, test, and deploy AI legal workflows and skills across firm-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization
• Partner directly with attorneys and knowledge management professionals to identify high-value use cases, map workflows, and translate practice needs into structured AI solutions
• Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney-ready documentation
• Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes
• Serve as a go-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption
• Develop and scale best-practice standards, use-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls
• Embed human-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements
• Collaborate closely with cross-functional stakeholders including the AI Product team, AI Adoption team, Knowledge & Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support
• Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities
• Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs
• Represent the AI Products & Adoption team in internal meetings, committees, and practice group sessions

AI Legal 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

• Partner with AI Solution Engineers, Architects, and senior technical teammates
• Design, build, and deploy AI-powered solutions for legal customers
• Translate legal workflows and business needs into technical solutions
• Contribute to technical discovery, solution delivery, and prototyping
• Assist with demonstrations, proofs of concept, and early-stage prototypes
• Develop working knowledge of legal terminology and processes
• Design and implement AI-powered solutions for legal workflows
• Collaborate with senior engineers and architects to ensure solutions are scalable
• Evaluate LLM outputs for legal accuracy and relevance

Software Engineer – AI, CoCounsel Forward Deployed Engineering

• Own the technical direction for Bumble’s recommendations platform, evolving the systems that service recommendations for millions of members.
• Lead the design of large-scale distributed systems that power recommendations across multiple teams and domains.
• Identify and solve the highest-leverage technical problems across the recommendations stack.
• Raise the bar for engineering quality through technical leadership.
• Multiply the impact of other engineers by mentoring senior engineers and influencing technical direction across teams.
• Drive platform evolution for the long term by leading foundational initiatives.
• Remain deeply technical through hands-on contribution.

Staff Software Engineer, Core Matching, Machine Learning

• Design, develop, and deploy production machine learning models supporting Torc's perception systems.
• Own end-to-end model development for scoped perception problem areas, from data preparation and training through evaluation and deployment.
• Write production-quality ML code to support scalable training, evaluation, and inference workflows.
• Analyze model performance, identify failure modes, and iterate to improve robustness, accuracy, and generalization across diverse driving environments.
• Develop and optimize perception models leveraging multi-modal sensor data, with an emphasis on camera-based and 3D perception systems.
• Collaborate with software engineers, infrastructure teams, and autonomy engineers to integrate perception models into production autonomy software.
• Support improvements to training pipelines, data workflows, and tooling that accelerate model iteration and deployment.
• Contribute to model architecture discussions and technical decision-making within the team.
• Mentor junior engineers on implementation, experimentation, and machine learning best practices.

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