Platform Engineer

• Collaborate with engineers across the company to understand and support their development workflows.
• Own technical projects from start to finish and be responsible for major technical decisions and tradeoffs.
• Lead complex projects that span teams, with many partners and customers.
• Effectively participate in planning, code reviews, and design discussions.
• Contribute impactful projects to the team’s roadmap.
• Thrive in a fast-paced, collaborative work environment where impact is a result of your efforts and where your contribution is highly visible.
• Drive software engineering best practices both within our team and across engineering.
• Create tooling that encourages and supports these practices.
• Mentoring and up-leveling other engineers on the team.
• Conduct technical interviews with well-calibrated standards and play a crucial role in recruiting activities.
• Be a positive contributor to our team culture.

Senior AI/ML Engineer – Autonomy CI Platform

• Design and implement embedded security solutions for next-generation automotive platforms.
• Translate high-level security and system requirements into scalable, secure embedded system designs.
• Collaborate with systems engineering to refine requirements and ensure feasibility in embedded environments.
• Partner with cybersecurity teams to define and implement security architectures and platform-level security components.
• Work closely with test teams to support validation activities, including test case development and issue resolution.
• Identify gaps in existing architecture and design, then build and integrate missing components end-to-end.
• Develop, maintain, and optimize embedded software using Embedded C as the primary language.
• Implement and support unit testing and participate in broader system and integration testing efforts.
• Utilize Git for version control and Jira for task/issue tracking to manage day-to-day development work.
• Contribute to secure services, key provisioning workflows, and cryptographic operations within the platform.
• Troubleshoot complex issues in a Linux-based embedded environment, including performance and security-related defects.
• Operate as a self-directed engineer, driving delivery of key platform milestones and contributing production-level code on aggressive timelines.

Staff Platform Security Engineer

• 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.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage 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.

Senior Lead AI Engineer, Gen AI Platform

• This Product Manager would be joining a team dedicated to building out and expanding operational data infrastructure, integrating machine learning models into our ecosystem, and working closely with partner teams to enable smarter segmentation and decisioning.
• Scope includes working with Enterprise platforms to identify new technology, patterns and data sources, business partners to identify the highest leverage areas for expanding capabilities, and with risk management to ensure our process works seamlessly end to end.
• Card Collections and Recoveries make up the Customer Resiliency team, and our product and business analyst teams share end-to-end ownership of the customer experience and drive meaningful P&L impact each quarter.

Manager, Product Management – Data Infrastructure

• Define and execute the enterprise Platform Engineering strategy supporting Consumer Products and Corporate Technology.
• Lead the evolution of engineering platforms that improve developer experience, platform consistency, scalability, and operational efficiency.
• Establish enterprise standards for platform architecture, engineering automation, and software delivery.
• Drive adoption of Internal Developer Platforms (IDPs), Infrastructure as Code, cloud platform engineering, Kubernetes, container platforms, release engineering, environment management, artifact repositories, secrets management, observability platforms, and platform networking.
• Build reusable engineering capabilities that enable product teams to deliver software securely and efficiently.
• Leadership, People Management, Budget & Vendor Management

Senior Director, Autonomous Platform Engineering

• Write and Maintain Software Solutions : Design, write, test, and deploy high-quality code to develop reliable, maintainable software solutions.
• Collaborate with Cross-Functional Teams : Work closely with platform engineers, product teams, and data scientists to ensure technical solutions effectively support business goals and content strategies.
• Production Support : Monitor and optimize production systems, ensuring platform stability, performance, and uptime.
• Mentorship: Provide technical guidance and mentorship to junior engineers, fostering a culture of collaboration and continuous learning.

Senior Software Engineer – Content Platform Engineering

• Build the Zoox Training framework leveraged by all ML teams within Zoox.
• Design, implement, and operate a robust and efficient ML platform to enable the training, validation, serving, and monitoring of ML models.
• Collaborate closely with cross-functional teams, including ML researchers, software engineers, and data engineers, to define requirements and align on architectural decisions.

Software Engineer, ML Platform – ML Training

• Lead the technical strategy and delivery roadmap for expanding Coupa's AI platform capabilities.
• Build and manage a high-performing engineering team across the US and India, hiring across ML, data engineering, and platform disciplines.
• Drive strategic partnership evaluations with AI infrastructure and model providers.
• Define and prioritize AI use cases that deliver the highest accuracy and business impact, working closely with product leadership.
• Expand model training, evaluation, and serving infrastructure beyond frontier model integrations.
• Own data strategy for AI, ensuring the right data pipelines, quality frameworks, and privacy-safe practices feed model development.
• Coordinate with existing AI platform, data platform, application platform, and cloud operations teams.
• Manage budget, hiring plan, and delivery milestones for the AI platform engineering organization.
• Present strategy, progress, and results to VP/SVP/C-level stakeholders.
• Ensure data safety, tenant isolation, and responsible AI practices across all AI capabilities.

Senior Director, AI Platform Engineering

• Serve as the hands-on technical lead for integrating OEM and white-label HPC AI/ML, general purpose compute, storage, and network hardware into Lambda’s HPC platform reference architectures.
• Drive the end-to-end process of new product introduction (NPI) for hardware systems, including system bring-up, documentation, vendor technical engagement, production readiness, and closure of hardware risks.
• Identify, debug, and resolve hardware issues across different hardware engineering domains during hardware NPI; support closure of critical fleet issues that require hardware design, vendor corrective action, or platform configuration changes.
• Partner with HPC architects to translate platform blueprints into concrete hardware selections and system configurations.
• Partner with the supply chain team on new vendor evaluation and QBR/HBR feedback on established vendors.
• Own the hardware platform through NPI, working with PMO to de-risk execution, drive cross-functional closure of hardware readiness issues, and ensure platforms reach production on schedule.
• Collaborate with the quality team and fleet reliability team during hardware NPI and after production to continuously improve product quality and reliability at scale.
• Work cross-functionally with fleet engineering, deployment, operation and datacenter engineering teams to ensure on-time delivery and deployment, quality, compatibility, performance, and scalability of new systems.
• Serve as the technical lead to evaluate, enable, and prototype new hardware in labs.
• Review BOMs to ensure configuration accuracy, component compatibility, and alignment of key commodities and components to Lambda platform requirements.

Senior HPC Platform Hardware Engineer

• Design & develop the next generation distributed ML data platform (Ingestion, Processing, Serving) using GCP and open-source frameworks
• Collaborate with stakeholders (ML & Data Engineers), translate needs & pain points into requirements, build self-serve capabilities and drive adoption
• Deliver technical projects owning major technical decisions and tradeoffs & contribute to the team’s strategic roadmap
• Champion engineering & operational excellence by continuously improving systems and processes
• Actively participate in team’s planning, code reviews and design discussions
• Conduct technical interviews, onboard new and mentor junior engineers

Software Engineer, Data Platform – Autonomous Vehicles

• Architect and build scalable tooling and services for client platforms, establishing the end-to-end technical strategy for platform automation and service delivery.
• Apply deep codified principles to ensure repeatable, auditable, and version-controlled changes across a global, heterogeneous client environment.
• Set and enforce technical standards for platform automation, mentoring other engineers and driving the adoption of modern software engineering practices across IT.
• Collaborate with Security, Networking, and Product teams to systematically improve platform reliability and the internal developer experience.
• Serve as the primary architect for sophisticated service delivery challenges, driving root-cause analysis and long-term remediation for core platform services.
• Influence and recommend changes to policies, standards, and architectural direction, and establish procedures that impact multiple teams or the broader organization.

Senior Staff Client Platform Engineer

Senior Lead AI Engineer, MLX, Agentic AI, Gen AI Platform Services

• Own the long-term technical direction of Roo's frontend platform, including the systems, tooling, and architecture that power development across web and mobile.
• Partner with Product and Design to evolve our design system as a foundational platform rather than a standalone library.
• Make our design system effortless to consume by engineers, designers, and AI agents through thoughtful integrations, documentation, tooling, and developer workflows.
• Lead platform initiatives like framework upgrades, React Native modernization, Expo adoption, and shared library evolution.
• Own the migration of legacy components to our v2 design system — including the tooling and documentation that let the rest of the team complete the migration themselves.
• Establish patterns that let engineers ship new pages and flows quickly from a component library that's built for how people actually build in 2026.
• Improve developer experience through shared tooling, automation, documentation, templates, and workflows that increase engineering velocity.
• Identify and eliminate the friction points that slow builders down — missing tokens, undocumented patterns, inconsistencies that force one-off decisions — and fix them systematically.

Senior Software Engineer, Frontend Platform

• Own the compute, orchestration, and infrastructure layer that CodeRabbit's AI engine and every product on top of it run on, across a multi-region GCP footprint and a product suite that's still growing.
• Build the platform function from the ground up, set the technical direction, establish the patterns everyone else builds on, and make the early architectural calls that are expensive to unwind later.
• Develop systems that need to be right, not just fast — everything else depends on them.

Senior/Staff Platform Engineer

• Design, build, and maintain AWS infrastructure as code using Terraform and Pulumi, with an emphasis on reusable modules, safe change management, and multi-account/multi-environment patterns
• Operate and evolve container platforms across ECS and EC2, including cluster architecture, autoscaling, networking, and cost optimization
• Drive our migration to microservices: decompose existing services, establish golden paths for service scaffolding, CI/CD, and deployment, and partner with product engineering teams through the transition
• Build internal tooling and automation in a general-purpose language (Python, Go, or TypeScript) — this role writes real software, not just configuration
• Own and advance our observability practice in Datadog: SLOs, dashboards, alerting strategy, distributed tracing, and driving down noise so on-call is humane
• Lead incident response and post-incident reviews; turn findings into durable platform improvements
• Mentor engineers, review designs and code, and set technical direction for platform standards across teams
• Champion security, compliance, and reliability best practices appropriate to a healthcare environment

Senior Platform Engineer

• Design, develop, deploy, and maintain production-ready, scalable API services
• Build platform solutions using microservices and containerized architectures
• Write and maintain unit, integration, and performance tests
• Develop and maintain internal and public API documentation
• Collaborate with engineering, product, and operations teams across the full development lifecycle
• Participate in Agile/Scrum ceremonies (stand-ups, planning, refinement, retrospectives)
• Support deployment, monitoring, and operational readiness of services in production

Senior Software Engineer – Data Platform

• Define and own the Data & BI platform strategy and multi-year roadmap, ensuring platform investments are aligned to the needs of federated AI and analytics teams across Pfizer
• Lead, develop, and grow a team of engineers and platform specialists across Platform Operations, Platform Development, and Data Reliability pods
• Establish and maintain product-minded ownership of the platform portfolio with roadmap priorities driven by user needs
• Own the vendor relationships and licensing strategy for the data and BI platform portfolio, including contract management, performance governance, and technology evaluation
• Partner with the Head of AI Ready Data to ensure platform infrastructure and data governance standards are tightly integrated
• Oversee the design, build, and operation of enterprise data pipelines, warehouse/lakehouse environments, and BI infrastructure at scale
• Define and publish platform SLAs covering availability, data freshness, query performance, and pipeline reliability
• Drive platform cost transparency and FinOps practices ensuring compute and storage costs are visible and continuously optimized
• Ensure all platforms meet Pfizer's enterprise security, privacy, and regulatory compliance requirements
• Serve as the primary platform interface for federated AI and analytics teams across Pfizer, embedding platform support and enabling self-service access
• Report regularly to the Chief AI and Data Officer on platform health, SLA performance, adoption metrics, and investment priorities

Head of Data, BI Platform Engineering

• Own the infrastructure layer for Pfizer's AI ambitions
• Manage the compute, LLM gateway, MLOps machinery, and observability platform
• Architect the AI workloads platform to support various Pfizer domains
• Build a governed, automated deployment pipeline for AI models
• Develop agent orchestration layer and observability infrastructure
• Monitor real-time usage and anomaly detection for AI systems
• Oversee MLOps platform, experiment tracking, model versioning, and deployment pipelines
• Ensure compliance with Trusted AI's governance policies

Head of AI – Agentic Platform Engineering

• Develop and implement GenAI applications leveraging: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, Prompt engineering techniques, Agentic AI concepts and workflows
• Build intelligent pipelines using frameworks such as LangChain, LangGraph, and Microsoft Foundry Agent Service
• Evaluate solution performance, accuracy, and scalability of GenAI implementations
• Contribute to the end-to-end GenAI lifecycle, including: Solution design and development, Integration and deployment, Performance tuning and optimization
• Support secure deployment, horizontal scaling, and operational stability of GenAI workloads
• Assist in implementing monitoring, logging, and observability practices for production environments
• Develop and deploy GenAI systems across cloud platforms (Azure and AWS)
• Contribute to distributed system design for scalable AI workloads
• Utilize modern infrastructure practices: Containerization (Docker), Orchestration (Kubernetes), Infrastructure as Code (Terraform, ARM/Bicep)
• Ensure solutions meet enterprise expectations for availability, performance, and security
• Develop scalable applications using Python and microservices-based architectures
• Apply secure coding standards and proper data handling practices for enterprise, regulated environments
• Contribute to CI/CD pipelines, automated testing, and deployment workflows
• Participate in code reviews and adhere to engineering best practices

Mid-Level AI Platform Engineer

• Define the long-term technical strategy and architecture for the enterprise ML Operations Platform.
• Design scalable, secure, and resilient cloud-native platforms supporting machine learning workloads.
• Establish best practices for model development, deployment, monitoring, and lifecycle management.
• Lead architecture for enterprise machine learning infrastructure supporting batch, streaming, and real-time inference.
• Drive adoption of cloud-native technologies, Kubernetes, and modern platform engineering practices.
• Define standards for model governance, observability, reliability, explainability, and responsible AI.
• Partner with infrastructure, security, and engineering teams to improve platform scalability, performance, and operational efficiency.
• Evaluate emerging technologies and recommend architectural approaches that improve platform capabilities.
• Mentor engineers and influence technical direction across multiple engineering organizations.

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