AI Engineer

• Report directly to our VP of Agentic AI
• Shape the core intelligence and behavior of the agent — how it interprets intent, plans, assists, and recovers from failure — so that it reliably helps users build working Unqork applications.
• Help scale the backend that powers the agent in production, contributing to a service that stays fast, observable, and resilient as usage grows.
• Drive how we measure and raise agent quality over time, turning real-world usage into a feedback loop that makes the product steadily more capable and trustworthy.

Staff AI Engineer

• Design, build, and operate systems in customer-facing production environments
• Translate ambiguous business and customer problems into prototypes & technical specs
• Track AI capabilities and apply them where they create clear leverage
• Own the full application lifecycle: product discovery, experimentation, evaluation, deployment, and monitoring
• Own system design, tradeoffs, and long-term scaling and maintainability
• Build code bases that are legible to agents and other developers; drive the organization forward on tooling
• Partner with product managers to enhance their product vision, including with AI-native solutions

• Help build next-generation AI-powered product experiences.
• Build AI-driven product features that integrate LLMs (like GPT, Claude, or Gemini) via APIs.
• Prototype quickly and iterate fast, turning rough concepts into working interfaces and tools.
• Develop front-end experiences in React that make complex AI outputs intuitive and delightful.
• Collaborate with backend engineers to design APIs and data pipelines that support AI interactions.
• Work across the stack when needed — from front-end interfaces to backend service integration in Python.
• Experiment with new LLM capabilities, libraries, and prompt engineering approaches.
• Contribute to internal tools for data cleaning, model evaluation, and content generation workflows.

• Own a product capability end to end, from architectural decisions through production outcomes, cutting through every layer: entry points, orchestration, intelligence, tools, data, and observability
• Your work directly shapes how Augeo's clients engage tens of millions of users
• Build and ship mission-critical AI agents that drive revenue growth across finance, healthcare, telecom, and commerce
• Define how AI agents are built at Augeo
• Drive the Agent Development Life Cycle (ADLC) from initial pilot through production iteration
• Build the shared platform: agent orchestration, agentic workflows, ML models, and data infrastructure
• Work across Augeo with engineers, product leads, and end users to understand needs, close feedback loops, and ensure the platform compounds with every interaction
• Shape the team and participate in hiring

AI Architect

• Provide strategic leadership and direction to 2 to 3 small, senior-caliber engineering teams
• Guide teams in understanding client needs, setting project goals, and ensuring successful implementation
• Manage 2–3 high-performing implementation teams of senior engineers
• Support the company’s rapid growth as we double our client base
• Drive delivery against the product roadmap
• Champion collaborations with TA and existing external sourcing vendors
• Refine onboarding methodologies
• Engage with organizational leaders to co-design and champion career progression frameworks
• Mentor an Engineering Talent Manager
• Dive deeper into analytics and connect the dots between talent metrics and broader business objectives
• Champion an AI-first engineering culture
• Facilitate the creation and management of data and information repositories
• Foster an environment of proactive domain-specific knowledge sharing among team members
• Pioneer refinements in delivery processes
• Advocate for and pursue expertise in advanced talent management strategies
• Maintain regular and reliable attendance

Senior Manager, AI Engineering

• Write and ship production AI code daily — you are an active contributor.
• Architect agentic AI workflows using LangGraph, Temporal, Pydantic — stateful, multi-agent workflows built for enterprise scale and reliability.
• Own AI observability via LangFuse: tracing, prompt versioning, evaluation, and performance benchmarking across all model interactions.
• Set AI engineering standards for agent design patterns, RAG, prompt management, context optimization, and tool-calling strategies.
• Partner with product and platform teams to deliver AI architectures that meet enterprise SLA, security, and compliance requirements.
• Evaluate and adopt emerging tooling — benchmarking LLM providers, orchestration frameworks, and agentic stack improvements.
• Mentor engineers as a natural extension of your work — sharing knowledge through code reviews, pairing sessions, and design discussions, not through management overhead.
• Represent Acquia's AI capabilities in customer architectural reviews, technical discovery, and roadmap conversations.

• Source relevant data from various channels, including databases, online sources, and internal systems, essential for training AI models.
• Process raw data to make it suitable for use in machine learning, which includes cleaning, normalizing, and segmenting the data.
• Create machine learning models using appropriate algorithms and techniques to solve specific business problems or enhance application capabilities.
• Rigorously test AI models to ensure their accuracy, reliability, and robustness, and validate them against predefined metrics.
• Seamlessly integrate AI models into existing software applications, employing API calls or embedded code for smooth functionality.
• Continuously optimize AI systems for better speed, efficiency, and accuracy, ensuring they meet the required performance standards.
• Identify and fix bugs or issues in AI models and their integration into applications, ensuring smooth operation.
• Keep abreast of the latest advancements in AI, machine learning, and related technologies to incorporate cutting-edge solutions.
• Work alongside other professionals, including AI Engineers, data scientists, software developers, and project managers, to achieve cohesive project goals.
• Create detailed documentation for AI systems and their architectures, aiding in maintenance, updates, and knowledge transfer.
• Utilize AI to improve the user interface and experience of applications, making them more intuitive and responsive.
• Adhere to ethical guidelines and standards in AI development, ensuring fairness, privacy, and transparency.
• Conduct in-depth analysis of AI systems' performance, identifying areas for improvement and optimization.
• Utilize cloud computing platforms for efficient AI model development, testing, and deployment.
• Incorporate user and stakeholder feedback into AI system development to continuously improve its effectiveness and usability.
• Serve as a bridge between technical and non-technical teams, facilitating the integration of AI into various business processes and ensuring alignment with business objectives.
• Partner with key stakeholders, including the business unit leaders, Product, Data & Technology teams, to assist with their AI-related technical issues.
• Contribute to the team of AI professionals by using expertise to answer questions and sharing repeatable design patterns with less experienced teammates, enhancing skillsets and competencies of team members, and sharing technical knowledge throughout the team.
• Participate in stakeholder reviews and design sessions.
• Provide data, reports, and information to management as needed.
• Drive strong communications, partnerships, and stakeholder management with senior leaders, functional managers, and staff.
• Perform other duties as assigned.

• This job is responsible for providing leadership, technical direction and oversight to a team delivering technology solutions.
• Key responsibilities of the job are to provide oversight of the design, implementation, and maintenance of complex computer programs, align technical solutions to business objectives, and ensure that coding practices/quality comply with software development standards.
• Job expectations include conducting multiple software implementations and applying both depth and breadth in knowledge of several technical competencies.
• This role is for a Feature Lead in the Generative AI Core Engineering Team, responsible for designing and delivering high-performance, scalable, and secure APIs to support Generative AI workloads.
• The Feature Lead will collaborate with cross-functional teams to ensure solutions meet business objectives while adhering to technical standards.

Feature Lead – Technology, GenAI Platform

• Lead, mentor, and grow a distributed team of AI engineers
• Act as a player-coach: review architecture and contribute to design
• Shape and execute the technical roadmap for the AI Engineering team
• Design and deliver agentic systems end to end
• Build and evolve our internal AI platform
• Partner with leaders from Finance & Supply Chain Systems
• Collaborate closely with peer leaders in Enterprise Architecture
• Champion modern AI engineering practices
• Develop OKRs for the team and report progress
• Recruit world-class AI engineering talent
• Contribute to AI & Business Technology Engineering leadership team planning

Manager, AI Engineering – AI & Business Tech Engineering

• Lead and Scale a High-Performing Pod
• Own the AI Orchestration Layer
• Modernize the Developer Experience
• Define Evaluation and Trust Standards
• Lead Foundational Model Strategy
• Be a Strategic Partner to Leadership

Product Lead, AI Platform

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components including foundation model training and large language model inference.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, and PyTorch.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance of large scale production AI systems.
• Contribute to the technical vision and the long-term roadmap of foundational AI systems at Capital One.

Senior Lead AI Engineer, Gen AI Platform

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Oversee the design, development, testing, deployment, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
• Attract and retain top talent in the AI industry and nurture personal and professional development for your team.
• Foster a culture of learning and staying abreast of the state-of-the-art in AI.

Senior Manager, AI Engineering – Gen AI Platform Services

• Design and build the core architecture for an enterprise workflow automation platform
• Make critical technical decisions about stack, architecture patterns, and implementation independently
• Create robust APIs, integrations, and workflow engines adaptable to diverse enterprise needs
• Deploy virtually or on-site into customer organizations across the US
• Conduct discovery sessions to understand workflows and requirements
• Rapidly prototype and build custom solutions tailored to each enterprise
• Iteratively integrate AI capabilities to enhance automation and drive value
• Act as the technical face of the company during customer engagements
• Work autonomously with minimal oversight from CEO and leadership
• Make architectural decisions and push back constructively when needed
• Move fast, ship quickly, and iterate. Success comes through iteration, not upfront planning

Lead AI Engineer

• Design, develop, and optimize advanced data pipelines using technologies such as Apache Spark, Kafka, Airflow, and cloud-native tools like AWS Glue, Azure Data Factory, or Google Dataflow to drive business insights and enable automation.
• Collaborate with data scientists, architects, and business stakeholders to transform raw data into actionable intelligence, while architecting and maintaining robust data solutions across data lakes, warehouses, and marts using platforms like Snowflake, Redshift, or BigQuery.
• Develop, deploy, and monitor AI/ML models with frameworks such as TensorFlow, PyTorch, and Scikit-Learn, and operationalize these models via APIs and batch or streaming services.
• Ensure data quality, security, and compliance by applying best practices in data governance, encryption, and access control, leveraging tools such as Apache Atlas, Collibra, or cloud security features.
• Research and prototype cutting-edge AI/ML algorithms, evaluate emerging technologies, and communicate findings through technical documentation, dashboards (e.g., Power BI, Tableau), and presentations.
• Champion best practices in CI/CD, automation, and code review to continuously improve the performance, scalability, and reliability of the organization’s data and AI ecosystem.

Lead Data/AI Engineer

• Own and improve applied AI and ML systems that influence important business workflows.
• Design, train, and deploy models that generate optimal, fair, and explainable offers for claims, balancing business objectives with fairness constraints.
• Build and refine production workflows around generation, decisioning, retrieval, evaluation, and orchestration.
• Work on concrete systems such as position statement generation, offer engine improvements, open negotiation agents, and configurable rules or decisioning systems.
• Design and run experiments that improve model quality, workflow quality, and business outcomes.
• Partner with product, operations, and engineering teammates to define success metrics and translate messy real-world problems into buildable systems.
• Improve prompt, model, and workflow behavior through tight feedback loops and practical iteration.
• Contribute to the engineering quality of these systems, including instrumentation, testing, rollout safety, and operational visibility.
• Use AI as a force multiplier in your own work and help the team move faster by bringing strong AI-native habits.
• Balance speed and rigor in an environment where shipping matters and iteration is constant.

Senior Applied AI Engineer – Offer Engine

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches
• Collaborate cross-functionally with product/business stakeholders to translate workflows into well-scoped deliverables

• Collaborate with cross-functional teams to design scalable AI architectures
• Contribute to innovative projects that leverage generative AI and large language models
• Develop and deploy cutting-edge AI solutions
• Work with advanced machine learning models and frameworks, focusing on cloud technologies and intelligent AI applications

• Architect and develop enterprise-scale multi-agent systems leveraging LLMs and autonomous agent frameworks using Google ADK, Agentspace, MCP, RAG, and A2A orchestration
• Design and implement RAG pipelines using BigQuery and Vertex AI Engine for knowledge grounding and factually accurate responses
• Optimize agents for orchestration, knowledge grounding, multi-step reasoning, and decision-making
• Design and implement distributed training workflows, online inference systems, and low latency serving architectures optimized for real-world performance, using Google cloud-native services
• Engineer scalable, secure, compliant and production-grade AI fabric and AI agent workflows using Vertex AI and modern cloud-native technologies
• Create reusable agent orchestration layers, observability hooks, and governance frameworks that accelerate Agentic AI adoption across TAG brands
• Partner with cross-functional stakeholders in translating business requirements into technical specifications
• Own the full AI development lifecycle – from data collection and implementation to deployment and monitoring
• Implement intelligent observability and automation strategies to ensure AI system reliability and performance at scale

• Manage mission-critical collaboration and content management systems
• Integrate AI capabilities such as intelligent search, workflow automation, and context-aware assistance
• Collaborate closely with stakeholders, system owners, security teams, and end users
• Stay current with Department AI initiatives and systems
• Design, develop, test, implement, and deploy SharePoint infrastructure and solution components
• Drive innovation within the M365 and SharePoint environments
• Conduct stakeholder consultations and workshops

M365 Automation and AI Developer

• Build and operate the LLM and agent infrastructure every team at Coinbase builds on.
• Ship high impact AI agents on top of the LLM infrastructure.
• Serve as the company's single path to LLMs and the substrate for the full agent lifecycle: build, deploy, run, observe, improve.

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