• Recruit, manage, and mentor a diverse team of Identity and Trust & Safety engineers, fostering a culture of collaboration, accountability, and continuous improvement • Provide hands-on mentorship, guidance, and decision-making to help engineering teams deliver effective, high-quality solutions • Own and drive execution on critical initiatives, ensuring timely, high-quality outcomes while promoting incremental delivery and impact • Define and evolve our Identity and Access management capabilities to meet future demands while shaping the Trust & Safety roadmap in alignment with company priorities and emerging threats • Design and implement scalable systems to detect, prevent, and respond to fraud and abuse, leveraging key metrics to measure effectiveness • Partner with Product, Security, Design, Engineering, Legal, and Compliance teams to embed security and safety considerations throughout the development lifecycle • Shape technical strategy and North Star Architecture while guiding the design and implementation of key services and components

Role Description This is a senior role for someone who leads from both directions at once — deeply technical on customer engagements, and fully accountable for the growth and performance of a team of ML engineers and architects. You will report to the Director of AI/ML. You own hiring, development, and team health alongside leading complex customer engagements, shaping architecture, and driving pre-sales. Both parts of this job are real and ongoing. The right candidate will find energy in that combination, not tension. Your Assignment - Leading Your Team - Hire and build: Set the technical bar for ML roles on your team, lead or oversee technical assessments, and make hiring decisions you can stand behind. Build a team that raises the practice's overall standard. - Develop people: Run regular structured 1:1s, provide candid feedback at meaningful milestones, and actively invest in each person's growth — whether they are early in their career or highly experienced. - Manage performance: Recognize strong contributors and address performance gaps directly and early. Partner with HRBPs and the Director of AI/ML when situations require a structured path, and advocate for your team when they deserve it. - Stay close to staffing: Understand how your team is utilized across engagements, keep the staffing team informed of each person's skills evolution and preferences, and ensure people are placed in work that stretches them appropriately. - Strategic Advisory - Lead ML assessments: Evaluate customer environments end-to-end — infrastructure, data pipelines, model lifecycle, and organizational readiness — and produce recommendations that drive executive decisions and open the door to the next engagement. - Shape architecture: Serve as the senior technical authority on engagements, setting architectural direction, ensuring technical quality across the team, and making the calls that matter when tradeoffs are hard. - Advise on ML operations: Help customers build ML systems they can actually own and sustain — translating MLOps, LLMOps, and production monitoring complexity into standards their engineering teams can execute and their leadership can act on. - Drive pre-sales: Partner with sales and solutions teams during scoping and proposal phases, contributing the technical depth needed to scope work accurately and give prospects confidence in Caylent's ability to deliver. - Hands-On Delivery - Lead engagements end-to-end: Drive architecture and solution design from kickoff through delivery — setting technical direction, unblocking the team on hard problems, and ensuring the work meets Caylent's quality standards. - Own the technical relationship: Depending on the engagement, you are either the primary client contact owning all architect-level outcomes, or the senior technical authority providing oversight across the team. The expectation is the same in both cases — you are the person the engagement depends on technically. - Growing the Practice - Raise the bar internally: Mentor engineers and architects through real work, contribute to technical interviews, and build reference architectures and accelerators that make the broader ML practice better. Qualifications - 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity. - Demonstrated people management experience — hiring, performance calibration, career development, and the ability to have difficult conversations directly and constructively. - Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring. - Deep expertise in at least two or three ML domains — whether classical ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape. - Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards that engineering teams can execute and executives can act on. - Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization, and distributed training — combined with RAG and agentic system design, including multi-agent architectures, MCP integration, and human-in-the-loop patterns on AWS. - Proven ability to operate independently in complex, ambiguous customer environments — navigating competing priorities, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences. Requirements - AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional. - Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements. - Exposure to varied industries and problem types in a consulting or client-facing context. - Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments. - Fluency in AIOps patterns — designing agentic workflows for anomaly detection, automated root cause analysis, and remediation across observability platforms — and the ability to translate AI operations outcomes into measurable business value for customers. Technical Stack - ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI - AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation) - Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts - AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly - Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue - Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate - Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama - MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness - LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation - Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo Benefits - 100% remote work - Medical Insurance for you and eligible dependents - Generous holidays and flexible PTO - Competitive phantom equity - Paid for exams and certifications - Peer bonus awards - State of the art laptop and tools - Equipment & Office Stipend - Individual professional development plan - Annual stipend for Learning and Development - Work with an amazing worldwide team and in an incredible corporate culture - This role may require up to 25% travel, depending on business needs.

Role Description The Data Engineering Manager – Enterprise Data Visualization will report to the Sr. Director, Data Engineering in the Technology Department. This role will engage with Business Leaders, Analysts, Data Stewards, Application Architects, and third-party providers. This role will lead an agile team of Data Engineers that work to enhance data delivery, quality, accessibility, and analysis. This role will improve functionality, streamline data processes, provide direct support to the business and operational teams, and strengthen targeted business strategies. The role works closely with the Business, Operations, and Technology groups to help design and lead the development and maintenance of the Enterprise Reporting Platform. If you are ready to advance your career and contribute to a rapidly growing company dedicated to delivering innovative products and ensuring an exceptional client experience, we eagerly await your application! Duties & Responsibilities - Lead an agile team of data engineers with varying levels of experience in the delivery of enterprise data initiatives. - Oversee the creation of data visualizations including enterprise dashboards, analytical, and operational reporting. - Partner with business leadership to implement 3–5-year plan for area of responsibility. - Manage multiple concurrent projects. - Define and establish benchmarks, metrics, and quality measures. - Support disaster recovery and contingency planning. - Ensure solutions meet non-functional requirements, including security, performance, maintainability, scalability, usability, and reliability. - Effectively manage relevant 3rd party vendor relationships. - Support the stability and resiliency of Data Visualization production processes, as well as instituting a robust support model addressing process and application failures. - Understand the business and technology. - Drive process alignment with business partners. - Identify project team requirements and capital requirements. - Evaluate and integrate productivity tools, development tools, testing tools, databases, and applications into this architecture. - Work with the leaders of Technology Infrastructure and Software Engineering to ensure effective operational tools and procedures are in place to support the application architecture. - Research and strategize emerging technologies relevant to business needs. - Develop and document an overall enterprise reporting delivery architecture which is fit for business purposes and cost effective. Supervisory Responsibilities - Recruits, interviews, hires, and trains new staff. - Oversees the daily workflow of the department. - Provides constructive and timely performance evaluations. - Participate in budget planning and monitoring. Qualifications - 10+ years of experience in Data Engineering, Data Visualization, or Software Product Development. - Bachelor’s degree preferred in Computer Science, Computer Engineering, Software Engineering, Electrical/Electronic Engineering, Mathematics, Statistics, Data Science, or similar/related Engineering/Science based disciplines. - 1-3 years of leadership experience managing direct reports. - Tableau Certifications are preferred. - Microsoft Certified Azure Data Fundamentals preferred. - Snowflake SnowPro Certification preferred. Skills & Abilities - Strong understanding of Programming Skills, knowledgeable in programming languages such as SQL/T-SQL, Python. - Competence with relational and NoSQL databases (e.g., SQL Server, MongoDB) including proficiency with Data Definition Languages, Data Mark-Up Languages. - Participate in design and implementation of OLAP databases to serve internal and external consumer use cases. - Design and implement hybrid data cloud services, leveraging public cloud providers (i.e., Azure) and specialty providers (i.e., Snowflake). - Understand and implement Generative AI solutions within the context of developer assistance and data visualization product delivery. - Support the development of enterprise data visualization strategy ensuring rapid delivery while taking responsibility for applying standards, principles, theories, and concepts. - Support enterprise data governance initiatives. - Strong experience in working with and optimizing enterprise reporting. - Exceptional analytical skills and strong attention to detail. - Ability to prioritize, plan and take initiative. - Highly self-motivated and directed. - Experience in a high availability environment preferred. - Knowledge of ITIL/ITSM Foundational practices and framework preferred. - Strong Vendor management skills preferred. - Strong understanding of Salesforce Financial Services Cloud data object model preferred. Data Engineering Tools/Platforms - Platform/Framework (Snowflake, Azure MSSQL). - Visualization (Tableau, PowerBI, SSRS). - Governance (Data.World, Atlan, Alation). Problem-Solving and Analytical Skills - Strong problem-solving abilities and the capacity to analyze complex technical challenges. - Ability to break down problems into manageable components and devise effective solutions. Software Product Development Lifecycle - Familiarity with the software development lifecycle (SDLC) is crucial. - Understanding requirements gathering, system design, implementation, testing, deployment, and maintenance in an Agile/Scaled Agile manner. - Experience with Scrum, Kanban, Extreme Programming, or other outcome-based iterative development approaches required. Knowledge of Development Tools and Frameworks - Proficient in using development tools and frameworks relevant to their domain, including version control systems (e.g., Git) and integrated development environments (e.g., Visual Studio Code, IntelliJ). Collaboration and Communication - Effective collaboration with cross-functional teams is vital for data engineers. - Strong communication skills, both written and verbal, enable them to clearly express ideas, collaborate with colleagues, and convey technical concepts to non-technical stakeholders. Continuous Learning - A mindset of continuous learning is crucial in the constantly evolving field of software engineering. - Staying updated with new technologies, programming languages, frameworks, and industry trends is highly valued. Testing and Debugging - Proficiency in automated software testing techniques, including unit testing, integration testing, and debugging, is important for ensuring the reliability and quality of software applications. Knowledge of Security Best Practices - Strong understanding of secure coding practices and the ability to apply them effectively in software development. - Ability to implement security controls, conduct code reviews, and perform security-focused testing, ensuring adherence to industry standards and minimizing the risk of potential exploits. Compliance - Familiarity with regulatory compliance requirements and industry-specific security standards, such as GDPR, HIPAA, PCI-DSS, and ISO 27001. - Ability to design and implement software solutions that meet these compliance standards, ensuring the protection of sensitive data and maintaining regulatory compliance. System Design and Architecture - Solid understanding of data platform system design principles and architecture patterns. - Includes scalability, performance optimization, and the ability to design robust and efficient software systems. Adaptability and Flexibility - Ability to encounter changing requirements, tight deadlines, and evolving technologies. - Being adaptable, flexible, and able to quickly learn and adapt to new tools and frameworks is crucial.

• Lead, coach, and develop a team of three engineers — setting clear expectations, providing regular candid feedback, and building individual growth plans tailored to each engineer's strengths, gaps, and career goals. • Establish and maintain healthy team operating rhythms: sprint cadence, backlog grooming, incident retrospectives, and async-first communication practices that work across time zones. • Build a culture of ownership, craft, and continuous improvement — where reducing toil and improving reliability is celebrated, not just expected. • Serve as an escalation point for complex technical issues and a knowledge resource for the broader IT Engineering organization. • Define and maintain the CPE roadmap in alignment with IT Engineering strategy, company-wide security priorities, and Affirm's FY roadmap cycles. • Establish a prioritization framework that balances project work, platform health (KTLO), compliance obligations, and stakeholder requests — with enough transparency that tradeoffs are visible and defensible. • Build and own the KPIs and reporting that give you, your team, and leadership real visibility into endpoint compliance posture, patch currency, deployment reliability, and incident trends. • Implement structured change control processes: communication, testing gates, rollback plans, and post-deployment review for platform changes that affect a global workforce. • Guide the architecture and long-term strategy for Affirm's endpoint platform — macOS-first, with Windows and mobile in scope — including MDM configuration, zero-touch provisioning, and the automation pipelines that keep the fleet healthy at scale. • Drive key technical initiatives including permission automation, third-party patching, silent update delivery, and device lifecycle improvements. • Champion infrastructure-as-code and automation-first engineering practices across the team — reducing manual toil and creating durable, repeatable processes. • Evaluate new tooling and approaches, run POCs, and make clear adoption recommendations grounded in security, reliability, and operational efficiency. • Build strong working relationships with Security, Identity/IAM, Developer Productivity, and IT Support — acting as a reliable partner and technical peer, not just a fulfillment queue. • Partner with Security to onboard and maintain endpoint security agents (EDR, AV, disk encryption) and enforce least-privilege policies at scale. • Represent CPE in cross-functional planning conversations, bringing clear data and grounded recommendations to tradeoff discussions. • Work closely with Developer Productivity to understand and support the unique endpoint needs of Affirm's engineering population.