Senior iOS Engineer (LATAM/Canada)
Develop and maintain the driver app and mobile SDK using modern Swift and agentic AI workflows. Focus on architectural improvements, quality automation, and refactoring legacy code to improve system health.
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Develop and maintain the driver app and mobile SDK using modern Swift and agentic AI workflows. Focus on architectural improvements, quality automation, and refactoring legacy code to improve system health.
Design and implement enterprise AI solutions using LLMs and RAG to support the Department of Veterans Affairs' AI summarization initiatives. Develop secure, scalable cloud-native applications and RESTful APIs while ensuring compliance with federal security and healthcare governance standards.
Lead the engineering effort to replatform and evolve a suite of proprietary eDiscovery tools, serving as the primary technical decision-maker for architecture and infrastructure. Establish engineering foundations including CI/CD and coding standards while building and mentoring a high-performing engineering team.
Design and maintain robust business data models to enable trusted sales analytics, reporting, and AI capabilities for Go-To-Market operations. Drive the automation of the BI ecosystem by treating dashboards and semantic layers as code using CI/CD pipelines.
The role involves providing technical support, troubleshooting, and enhancing application features while contributing to design and development discussions. Responsibilities also include creating migration plans for legacy systems and implementing secure unit and systems testing.
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The role focuses on building, maintaining, and optimizing scalable data pipelines and architectures to power business-critical analytics. It involves collaborating with Data Science and Ops teams to ensure data quality and enable self-service analytics.
The MLOps Engineer is responsible for the reliable and scalable deployment of AI and Machine Learning solutions across enterprise environments. This involves bridging the gap between data science and production by building reusable frameworks and automating ML model operationalization.
Design and deploy AI/ML and Generative AI solutions to build intelligent data products and digital experiences. Develop full-stack applications and cloud-native data solutions to enable self-service analytics and operational insights.
The Senior Analytics Engineer will lead the development and maintenance of the transformation and semantic layer using dbt and Snowflake. They are responsible for building governed, reusable data models and mentoring junior engineers to ensure high-performance enterprise analytics.
Lead and develop the US technical team while governing the quality and delivery of cloud and AI deployments. Partner with sales to identify expansion opportunities and translate technical strategies into business value for enterprise clients.
Lead the direction and reliability of the internal engineering platform to enable software and data science teams to deploy workloads in Microsoft Azure. Establish the platform roadmap, define technical standards, and provide hands-on leadership for shared infrastructure and delivery pathways.
Design and maintain business-critical AI agent services and production microservices using LangChain. Lead system architecture decisions and mentor junior engineers to improve engineering delivery through agentic development.
Lead the technology function by owning the technical roadmap and driving modernization efforts to support business goals. Build and develop high-performing engineering teams while managing cybersecurity risks and evaluating emerging technologies like AI.
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Lead and grow the team responsible for Fellow's recording platform across desktop, mobile, and real-time media infrastructure. Act as a player-coach by managing engineers, setting technical direction, and contributing production code to improve reliability and scalability.
The role involves owning the architecture, design, and engineering of core STARLIMS software, including user-facing applications and data management logic. It also requires managing the full SDLC, providing system support, and mentoring junior team members.
Lead the technical direction and architecture for an enterprise API platform, including the developer portal and API catalog. Drive the integration of AI-powered capabilities and MCP servers to enhance the developer experience across the organization.
Define and own the API and graph architecture strategy to decouple client-facing experiences from underlying systems during a major migration. Provide cross-organizational technical leadership to establish enterprise-wide standards and scale innovative AI-driven workflows.
Lead the architecture and vision for major client-facing digital platforms to ensure scalability and future readiness. Provide cross-organizational technical leadership, mentoring engineers and defining enterprise-wide technology standards.
Lead the development of modern SaaS solutions while fostering an AI-first engineering culture through the use of AI coding tools. Manage direct reports through coaching and performance management while overseeing the technical design and delivery of secure, scalable Azure services.
Design, build, and maintain scalable ETL/ELT data pipelines and data models to support analytics and machine learning at petabyte scale. Develop production-quality Java and Python code for streaming and batch processing systems within a cloud infrastructure.
Design and maintain scalable, secure infrastructure and tooling to enable Software Defined Manufacturing. Collaborate across teams to architect and deploy the software stack from robot to cloud while improving developer velocity.
Lead the system development lifecycle for complex software systems supporting wealth management and retirement solutions. Manage and mentor engineering teams while partnering with business leaders to ensure optimal system performance and delivery of business value.
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The role involves designing, developing, and supporting software application systems to meet business needs across various technical areas. Key duties include translating requirements into specifications, performing code reviews, and providing Level 3 operational support.
Build and implement enterprise contact center transformations using Amazon Connect, Generative AI, and serverless AWS architectures. Develop AI agents, custom agent tooling, and infrastructure as code while collaborating directly with customer engineering teams.
Build and implement enterprise contact center transformations using Amazon Connect, AI agents, and serverless AWS architectures. Develop AI-native self-service experiences and custom agent tooling while collaborating directly with customer engineering teams.
Build and implement enterprise contact center transformations using Amazon Connect, Generative AI, and serverless AWS architectures. Develop AI agents, custom agent tooling, and scalable infrastructure while collaborating directly with customer engineering teams.
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