We are hiring someone to lead product level and internal team AI end-to-end: from protocol and architecture decisions to server-side implementation to the AI logic that decides when and how our tools are used, to internal processes that improve R&D and product development efficiency. This is a hands-on position with managerial aspects, not an advisory role. You will be building hands on in parallel to building the process and managing/guiding the team.

What You Will be Doing
Own the strategy and roadmap for C2As AI strategy within R&D and AI agents specifically,
Lead the AI adoption effort technically within the product and across the organization as the company learns how to become more efficient with AI usage for R&D.
Design and build MCP servers that expose our APIs as well-described, reliably usable tools for AI clients.
Build the AI decision layer - the prompting, tool schemas, and agentic logic that let a model select the right API, pass the right parameters, and recover gracefully from errors.
Implement complete, production-grade features end to end: backend services, data models, APIs, and the AI integration on top, not prototypes.
Define how AI-accessible APIs are scoped, authenticated, and permissioned, so an agent can only ever do what it is authorized to do. Security is the product, this matters.
Build evaluation and observability for the AI layer: measure tool-selection accuracy, catch regressions, and trace agent behavior in production.
Set engineering standards and mentor others as the effort grows into a team.
Implement new features required by our cybersecurity product.

We are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

You will manage the AI Platform Engineer(s), set the technical standards for the AI Power User group's citizen development program, and serve as the connective tissue between business leadership, platform owners, and development teams. You will shape the multi-year AI architecture roadmap while also rolling up your sleeves to conduct architecture reviews, resolve blockers, and move use cases from concept to production. This is a role for someone who can think big and execute - and who understands that in an enterprise context, the quality of your governance is inseparable from the quality of your architecture.

What You'll Own
Strategy & Architecture
Define and own the enterprise AI integration strategy - identifying opportunities to embed intelligent automation, agentic workflows, predictive analytics, and generative AI capabilities across our company core platforms
Develop and maintain reference architectures, design patterns, and the AI architecture decision log that governs how AI models connect to enterprise systems and what they are permitted to do
Consult on enterprise system architecture and implement best practices for the Enterprise Business Systems team to leverage in their day-to-day execution.
Lead Proof-of-Concept initiatives for new AI tools and platform-native AI features, evaluating them against build-vs-buy criteria before recommending adoption
Partner with business stakeholders to translate operational pain points into AI use cases with clear ROI framing and sequencing criteria
Contribute to our enterprise data strategy, ensuring AI initiatives are supported by clean, accessible, and well-governed data pipelines
Integration Architecture & Delivery

Design and own the Workato eMCP layer - the MCP governance model, persona-scoped token framework, workspace isolation strategy, and the single sanctioned action surface through which all AI agents write back to enterprise systems
Define integration patterns and standards for AI model connectivity (Claude, ChatGPT) to Salesforce, NetSuite, HiBob, and Jira - specifying what agents can read, what they can write, through which surfaces, and with what confirmation and audit requirements
Design and oversee API strategies, event-driven architectures, and middleware patterns that support scalable AI feature delivery - including agentic workflows, intelligent data transformation, anomaly detection, and natural language interfaces layered onto ERP and CRM data
Collaborate with Engineering during build phases, conducting architecture reviews, providing hands-on guidance, and resolving complex technical blockers
Define non-functional requirements - latency, security, auditability, model drift monitoring - for AI components embedded in mission-critical business processes
Establish MLOps and LLMOps practices appropriate for our enterprise environment: model versioning, observability, and rollback procedures for production AI workloads

We are looking for a strong Backend Engineer with 5+ years of experience to help build the core infrastructure behind our company's agentic AI system. This is not another workflow tool: we are building something new, in a category that does not really exist yet. You will work on the services, orchestration, integrations, data models, retrieval layers, guardrails, and evaluation infrastructure that let AI agents reason over enterprise context and take reliable action.
This role is deeply product-facing and infrastructure-heavy. Our customers are large enterprises that manage tens of thousands of identities, so the systems we build need to be secure, observable, reliable, and able to operate at real scale.
What You'll Do:
Build production backend services in Python that power our company's agentic AI platform.
Design and implement AI-agent orchestration, tool-calling infrastructure, workflow execution, guardrails, and evaluation loops.
Integrate with enterprise applications, identity providers, SaaS tools, and customer data sources so agents can understand context and execute tasks safely.
Build systems that ingest, model, and reason over organizational processes, permissions, policies, and internal knowledge.
Solve scale and reliability challenges for large enterprise environments with tens of thousands of identities.
Collaborate closely with product, AI, and customer-facing teams to turn ambiguous business processes into reliable automated experiences.
Own reliability, observability, performance, and security for production systems.

We are looking for a Senior II Software Engineer to join the CX Platform team, the foundational engineering team powering our entire Customer Experience group. This is a high-impact, hands-on role at the intersection of backend engineering, AI infrastructure, and customer-facing product.

You'll work across the full platform stack (backend services, data pipelines, security, cost, and scale) with a meaningful and growing focus on AI infrastructure. We own the agentic platform for the entire Product Offering group: from building the LLM infrastructure and agentic workflows to ensuring they're reliable, observable, and safe in production.

What you'll be doing:
Own AI infrastructure for the Product Offering group. Design, build, and evolve the shared AI platform (agentic workflows, LLM integrations, observability, and guardrails) that CX product teams build on.
Ship agentic features end to end. Lead development of AI-driven capabilities using LangChain, LangFuse, and AWS Bedrock, from architecture through production deployment and monitoring.
Drive platform architecture. Set the technical direction for the CX backend (services, data pipelines, API patterns) with an eye for scalability, reliability, and developer experience.
Own core data foundations. Design resilient data-access patterns across Snowflake, Elasticsearch, Kafka, Redis, and MySQL; keep pipelines fast, fresh, and reliable.
Mentor and elevate. Help engineers across the CX group grow in backend craft, AI engineering, and system design thinking.
Collaborate cross-functionally. Work with product, design, and customer-facing teams to turn ambiguous problems into well-scoped, high-quality solutions.

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how evolves from AI-enhanced internal tools to customer-facing AI products.

We are looking for a Senior Software Engineer to own and evolve the runtime platform that powers production AI agents.

This is a hands-on backend and platform engineering role for someone who combines deep TypeScript expertise, strong distributed-systems fundamentals, and exceptional production debugging skills with a practical understanding of LLMs and agentic systems.

You will work closely with engineers building AI agents. Your responsibility will be to provide the reliable runtime, infrastructure, abstractions, and observability they need to deliver new capabilities safely and quickly.

What youll do

Own and evolve the production runtime responsible for executing and orchestrating AI agents.
Design platform capabilities for agent execution, tool calling, streaming, state management, persistence, and long-running workflows.
Build resilient integrations with multiple LLM providers and model-serving platforms.
Design provider-routing and fallback strategies based on availability, latency, quality, and cost.
Implement retries, timeouts, circuit breakers, rate-limit handling, idempotency, and graceful degradation.
Ensure the platform remains available when external dependencies or infrastructure components experience outages.
Build reliable mechanisms for loading, caching, versioning, and recovering agent configurations and artifacts.
Create end-to-end observability for AI requests, including model, provider, agent, latency, token usage, cost, errors, retries, and fallback behavior.
Define dashboards, alerts, SLOs, and runbooks for production AI workloads.
Lead the investigation of complex production issues across application code, infrastructure, external providers, distributed state, and agent behavior.
Improve platform scalability, concurrency, latency, and resource efficiency.
Build reusable APIs and abstractions that allow agent developers to add capabilities without duplicating infrastructure logic.
Strengthen platform quality through integration testing, load testing, failure injection, and dependency-outage simulations.
Turn production incidents into architectural improvements, automated tests, monitoring, and operational safeguards.
Collaborate with product, infrastructure, and engineering teams to translate customer and business requirements into platform capabilities.
Mentor engineers and establish best practices for building and operating reliable production AI systems.

What you'll do:
Build the surfaces agents call directly: API-first account provisioning, machine-to-machine authentication, scoped credentials, and agent-level identity and attribution.
Own our MCP (Model Context Protocol) servers and an agent-first CLI - let coding agents and LLMs discover and invoke our reliably, engineered for agentic callers: token-efficient tool schemas, machine-parseable output, and loud, actionable failures instead of silent ones.
Harden the agent media path - make upload and core media operations reliable for non-human callers, eliminating the ambiguity and silent-failure modes that cause agents to drop off mid-task.
Make agent behavior measurable - partner on the instrumentation, signals, and attribution that tell us whether agents are succeeding.
Work across a polyglot backend - Node.js/TypeScript for the agent tooling layer, with Ruby/Rails and Go in the core platform.
Ship, measure, iterate - prototype quickly, dogfood the experience, and represent the agent's perspective in every design decision. Work closely with Product, Ecosystem, and Discoverability across the AX team.