We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are

An AI Native Engineer with a strong foundation in building cloud-native solutions and hands-on experience designing and deploying agentic systems, especially for enterprise environments. You’re a critical thinker who thrives in ambiguity, delivering concrete results by designing, building, and running AI agents that augment workflows and scale across modern infrastructure.

You'll shape how enterprises adopt AI-native engineering - either by leading complex agentic solutions and developing engineering talent, or by owning critical technical areas end-to-end as a senior IC

The Work

You’ll partner directly with client stakeholders — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be net-new platforms and systems that need to be stitched together in our clients’ environments alongside our ecosystem partners.

Agent Architecture & Engineering

• Design and build enterprise-ready AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
• Implement resilient, testable, and maintainable agentic workflows that can be iterated on quickly.

AI Platform Integration

• Develop and/or extend abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to enable seamless integration and multi-provider enablement.
• Contribute to shared libraries, SDKs, and patterns that can be reused across clients.

Cloud-Native Engineering

• Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability stacks to deliver scalable AI-native systems.
• Own deployment, monitoring, and troubleshooting for your services in production.

Domain-Specific Workflows

• Tailor and deploy agentic applications across verticals (e.g., finance, healthcare, retail), adapting to domain-specific processes and constraints.
• Work closely with client SMEs to translate business workflows into agentic solutions.

Client Engagement

• Participate in and/or lead design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption.
• Communicate trade-offs, risks, and recommendations clearly to both technical and non-technical audiences.

Measure & Improve

• Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
• Iterate rapidly based on data, feedback, and changing requirements.

Knowledge Sharing

• Craft reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.
• Contribute to internal communities of practice around AI-native and agentic engineering.

Travel may be required for this role. The amount of travel will vary from 25% to 75% depending on business need and client requirements.

Key Responsibilities

• Use AI coding assistants daily as a standard part of delivery, actively, frequently, and with demonstrable impact on productivity and output quality
• Integrate LLM APIs into applications in production: calling AI provider APIs in live code, managing token limits and latency, and building initial abstraction layers
• Apply AI across the full software delivery lifecycle: AI-generated tests, AI-assisted debugging, AI-accelerated code review, and prompt engineering for development tasks
• Own the quality of AI-generated outputs in your delivery scope, exercise engineering judgment about reliability, limitations, and failure modes; know when AI output is production-ready and when it is not
• Define and track KPIs to evaluate the effectiveness and ROI of AI-assisted workflows; present AI productivity and quality metrics to project stakeholders
• Own delivery end-to-end — from design through to production support — in Agile sprint cycles alongside client engineering teams
• Contribute to shared knowledge bases, reusable components, and internal AI tooling standards that benefit the wider team
• Build and integrate the application layers, APIs, and interfaces that connect full-stack systems to agentic backends — understanding data flows, context handoffs, and integration points between your code and AI pipelines

Basic Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field
• Comercial software engineering experience in production environments (or equivalent demonstrated through academic projects, internships, or shipped personal projects)
• Proficiency in at least one primary backend language: Python, Java, or TypeScript
• Demonstrated hands-on experience using AI tools actively in day-to-day engineering work — with practical examples of how AI was used to solve real problems, iterate on outputs, and improve delivery; including direct experience calling LLM APIs in production code with an understanding of token management, latency, and cost tradeoffs
• Basic understanding of web technologies including JavaScript, HTML, and CSS
• Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines
• Understanding of Agile delivery fundamentals
• Experience with databases — SQL or NoSQL
• Ability to validate, evaluate, and improve AI-generated outputs; understanding of AI limitations and responsible use
Familiarity with agentic system concepts — awareness of orchestration frameworks (LangChain, LangGraph, or equivalent), RAG pipelines, and how full-stack applications connect to agent-based architecture; production experience preferred, conceptual understanding required

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