Design, develop, and implement AI-enabled applications and tools based on defined requirements, collaborating heavily with Process Excellence, SMEs, End Users, and IT teams.
Responsibilities
Design, develop, and implement AI-enabled applications and tools based on defined requirements
Participate in process re-engineering efforts applying systems engineering and lean practices
Translate process and functional requirements into working software solutions with user-friendly interfaces
Plan software structure including data models, APIs, front-end/back-end structures, and AI/LLM integrations
Participate in rapid development cycles with other developers using AI-assisted coding tools
Software Engineer responsible for end-to-end ownership of product domain, designing, building, testing, and operating high-performance systems.
Responsibilities
Design, build, and maintain scalable, high-throughput, and resilient microservices and event-driven patterns.
Take complete ownership to analyze requirements, architect and build domain models across the stack, and reliably deliver high-impact software using polyglot tools and modern deployment pipelines.
Take end-to-end ownership of test automation by writing comprehensive unit, integration, and smoke tests to maximize test coverage and prevent production bugs or incidents.
Drive operational excellence and system reliability by actively monitoring live application metrics, dashboards, and system health to proactively detect bottlenecks and resolve production issues.
Architect event-driven workflows and distributed systems to ensure robust, real-time synchronous and asynchronous communication across services.
Build the internal Experience Center portal, integrations, APIs, and data-backed views for operations specialists using a modern AWS serverless stack and Snowflake.
Compensation
Competitive salary and annual incentive bonuses to reward your contributions.
Responsibilities
Work up and down the full stack to ship features end to end
Build the internal Experience Center portal that specialists use
Build and maintain integrations that bring customer context, AI-drafted responses, and back office data to specialists
Develop the API and service layer that classifies inbound inquiries and routes work
Build data-backed views and reporting against Snowflake
Requirements
Bachelor’s degree in Computer Science, Information Technology, Software Engineering, or equivalent practical experience.
The ML Engineer will build, deploy, and maintain production-grade machine learning services and tooling in the advertising domain, focusing on MLOps and SRE principles.
Responsibilities
building, deploying and maintaining production-grade machine learning services and specialised tooling
designing and orchestrating ML engineering workflows, including model packaging, deployment strategies, observability and operational stability
productionising machine learning models in close collaboration with Data Science and Research teams
developing and maintaining automated deployment pipelines, model monitoring, alerting and rollback strategies
ensuring reproducible model packaging and reliable model lifecycle management
Join the Nitrous Network Automation team within Electronic Trading Services to design, build, and support software that improves network automation capabilities and developer/operator workflows.
Compensation
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.
Looking for a Senior QA Automation combining strong test automation expertise with a passion for AI-powered engineering, focusing on frontend automation while contributing to backend and integration testing.
Responsibilities
Design, develop, and maintain automated test frameworks and suites, primarily for frontend applications, using Java and Selenium with Cucumber.
Develop and execute UI, API, integration, regression, and end-to-end tests.
Leverage AI coding assistants, CLI-based agents, and LLMs to accelerate test development, debugging, and maintenance.
Utilize MCP (Model Context Protocol) and prompt engineering to enhance automation workflows and engineering productivity.
Analyze test results, investigate failures, report defects, and collaborate with developers on their resolution.
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