The ad says“Expect to be hands-on across React frontends, backend microservices, APIs, data models, asynchronous workflows, agent orchestration layers, and delivery infrastructure. Because these agents work with sensitive HR and financial data at global scale, you’ll help implement the guardrails that keep them private, predictable, and explainable”
The ad says“Build and operate end-to-end features across React frontends, backend microservices, APIs, data models, and CI/CD. You build it, you run it”
The ad says“Hands-on with Infrastructure-as-Code, CI/CD automation, Docker and Kubernetes, observability, incident support, and secure coding practices”
The ad says“Hands-on with Infrastructure-as-Code, CI/CD automation, Docker and Kubernetes, observability, incident support, and secure coding practices”
The ad says“Hands-on with Infrastructure-as-Code, CI/CD automation, Docker and Kubernetes, observability, incident support, and secure coding practices”
The ad says“Deep knowledge of backend service architecture, microservice patterns, and REST API design, with proficiency in Python and ideally Kotlin or Java”
The ad says“Expect to be hands-on across React frontends, backend microservices, APIs, data models, asynchronous workflows, agent orchestration layers, and delivery infrastructure. Because these agents work with sensitive HR and financial data at global scale, you’ll help implement the guardrails that keep them private, predictable, and explainable”
The ad says“Strong experience with React or a comparable framework, TypeScript, state management, and modern build tooling, plus a commitment to accessibility (WCAG 2.1/2.2)”
Values
The ad says“Experience integrating ML services or data pipelines into web applications; familiarity with Elasticsearch, rich-text editors or document-parsing tools, C4 diagramming, or Workday XO lifecycle management”
The ad says“Deep knowledge of backend service architecture, microservice patterns, and REST API design, with proficiency in Python and ideally Kotlin or Java”
The ad says“Deep knowledge of backend service architecture, microservice patterns, and REST API design, with proficiency in Python and ideally Kotlin or Java”
The ad says“5+ years with relational databases (PostgreSQL preferred) and non-relational stores, including query analysis and indexing optimization”
You’ll own the end-to-end design, implementation, and product integration of our intelligent agents — and build the full-stack product surface those agents live in. Our ML Engineers build and optimize the foundational algorithms; your mission is intelligence orchestration and product delivery, connecting the brain to the product
Expect to be hands-on across React frontends, backend microservices, APIs, data models, asynchronous workflows, agent orchestration layers, and delivery infrastructure. Because these agents work with sensitive HR and financial data at global scale, you’ll help implement the guardrails that keep them private, predictable, and explainable
This is the most senior engineering role on the team. You’ll set the technical direction for our agent architecture, mentor the engineers around you, and be the person who presents the work when the stakes and visibility are highest
Design and implement agentic workflows — orchestration and routing layers, tool calling, retrieval pipelines, memory, and multi-step reasoning — and integrate foundation models into customer-facing HR and Finance workflows
Build and operate end-to-end features across React frontends, backend microservices, APIs, data models, and CI/CD. You build it, you run it
Own the constraints that make agents viable in production: latency, cost per interaction, context-window efficiency, determinism, and graceful failure
Build the evaluation loop — offline and online evals, benchmarking agent behavior against product requirements, and production observability of agent traces
Implement Responsible AI in practice: guardrails, permission and data-boundary enforcement, PII handling, auditability, and explainability
Lead high-visibility projects end to end and present them with confidence in design reviews, demos, and executive updates
Make the call when the team needs one — gather input quickly, commit to a direction, document the trade-offs, and adjust as evidence arrives
Mentor engineers, raise the technical bar, and partner closely with Product, UX, Machine Learning, and platform teams
Preferred experience
4+ years using cloud platforms (e.g., AWS, GCP) to deploy responsive, scalable systems
2+ years integrating large models (LLMs, foundation models) and modern AI APIs into production, user-facing products
5+ years with backend web frameworks; Python frameworks such as FastAPI, Flask, or Django preferred
10+ years of professional software development experience, including significant full-stack ownership from modern frontend through backend services
1+ years designing and scaling AI orchestration architectures — multi-agent or tool-calling frameworks, routing layers, or advanced RAG pipelines
Strong grasp of distributed-system trade-offs, with the ability to independently diagnose and optimize performance at scale
Highly autonomous builder who turns open-ended product goals into concrete, scalable engineering plans
A natural technical leader: people follow your direction because of your clarity and judgment, not your title. You measure success by what the team ships, not only by what you ship
Strong understanding of the governance, guardrails, and security layers required when deploying autonomous agents over sensitive enterprise data
Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or an equivalent technical field
Deep knowledge of backend service architecture, microservice patterns, and REST API design, with proficiency in Python and ideally Kotlin or Java
Strong experience with React or a comparable framework, TypeScript, state management, and modern build tooling, plus a commitment to accessibility (WCAG 2.1/2.2)
Proven ability to architect the application layer around AI models: reusable patterns for predictability, error handling, and seamless UX integration
Practical command of LLM constraints (token and cost management, context-window efficiency, caching, streaming, fallbacks) and of evaluation — rapid prototyping, benchmarking outputs, and automated metrics for retrieval quality and agentic behavior
Collaborative and decisive: you seek input broadly, disagree and commit, and keep momentum without steamrolling
Hands-on with Infrastructure-as-Code, CI/CD automation, Docker and Kubernetes, observability, incident support, and secure coding practices
Experience integrating ML services or data pipelines into web applications; familiarity with Elasticsearch, rich-text editors or document-parsing tools, C4 diagramming, or Workday XO lifecycle management
Confident presenter who is comfortable owning the room, and steady when a project is visible, ambiguous, or under real time pressure
Product-first mindset: you apply large models to solve practical user problems, not to showcase the technology
Track record of shipping AI and agentic features that real customers use in production, and owning them after launch — prototypes alone aren’t the bar
5+ years with relational databases (PostgreSQL preferred) and non-relational stores, including query analysis and indexing optimization
Proven track record leading engineering workstreams end to end and mentoring junior-to-mid-level engineers into stronger ones
