Forward Deployed Engineer

As a Forward Deployed Engineer at Interactive AI, you will operate at the frontier of autonomous systems and real-world business impact. You won’t just be building PoCs or isolated pilots; you will be deploying production-grade agentic solutions that solve critical business bottlenecks and drive measurable impact across a diverse range of industries. 
Your mission is to bridge the gap between abstract reasoning and production-grade reliability. You will own the end-to-end journey: from deeply understanding a customer’s unique business case and identifying technical pre-requisites to deploying our proprietary agentic framework and platform to resolve the business challenges at hand.
Crucially, you are a core part of our innovation engine. By deploying in the field, you will identify framework limitations and directly contribute code and feedback to our core engineering team, ensuring our agentic architecture and platform evolve alongside the most demanding real-world use cases.
1. Autonomous Agent Deployment & Architecture

• Master and Deploy Interactive Agents: Develop a first-principles understanding of our proprietary framework to architect multi-agent orchestrations and adaptive reasoning loops within customer environments.
• Lead Technical Scoping: Independently translate ambiguous business problems into executable agentic architectures, defining the necessary tool-use requirements and safety guardrails.
• Synthesize Business Context: Deeply analyze the unique challenges of customers—ranging from agile medium-sized companies to large enterprises—to design tailored agentic strategies that drive measurable ROI.

2. Custom Tooling & System Implementation

• Build Cognitive Extensions: Engineer MCP (Model Context Protocol) tools, CLI interfaces, and custom Python/Node.js backend services that allow agents to interact fluently with diverse customer tech stacks.
• Implement Memory & Data Pipelines: Build state management systems and data pipelines to ensure agents maintain long-term context and remain grounded in accurate, customer-specific data.
• Bridge the Integration Gap: Identify and implement the necessary pre-requisite integrations (APIs, databases, ticketing systems) required for agents to effectively resolve the target business case.

3. Framework Evolution & Product Feedback

• Close the Innovation Loop: Act as the primary bridge between the field and core engineering, providing direct code contributions and real-world performance insights to improve our underlying agentic framework and platform.
• Scale Through Best Practices: Author reusable deployment patterns, agent blueprints, and "best-in-class" tool-use practices that accelerate the implementation cycle for future customers.

4. Agent Reliability & Production Ownership

• Orchestrate & Scale Workloads: Deploy and manage containerized agent environments within the InteractiveAI platform, optimizing for the unique resource demands of the business problem being solved.
• Ensure Production Excellence: Own the end-to-end reliability of the solution, troubleshooting autonomous decision-making issues and ensuring agents are tuned for self-correction in live settings.

We’re looking for an engineer who thrives in customer-facing, execution-heavy environments and enjoys owning delivery outcomes end to end.
Minimum Requirements

• 5+ years of experience in backend engineering, systems integration, or delivery-focused roles.
• Strong proficiency in Python or Node.js, with the ability to write clean, performant code for agentic orchestrators and toolsets.
• Proven track record of building complex system integrations, including APIs, connectors, and custom backend services.
• Technical Problem Discovery: Strong communication skills and the ability to translate high-level business problems into executable engineering work for both medium and large-sized customers.
• Agentic Exposure: Previous experience building or deploying agent-based systems, multi-agent frameworks, or RAG-based applications.
• MCP Ecosystem: Familiarity with the Model Context Protocol (MCP) or building custom CLI-based tools for LLM interaction.
• Hands-on experience with RESTful and/or GraphQL APIs, and the ability to translate these into functional tools for LLM consumption.
• Strong familiarity with Cloud Infrastructure (AWS, GCP, or Azure) and experience deploying/managing Docker-based workloads.
• Solid understanding of Data Architecture, specifically relational databases (Postgres) and in-memory stores (Redis) within integration contexts.
• High Degree of Autonomy: Ability to walk into an ambiguous business setting, identify technical pre-requisites, and architect a solution from scratch.

Nice-to-Haves

• Experience with Kubernetes: Proficiency in managing, scaling, and troubleshooting containerized workloads at the orchestration level.
• Infrastructure-as-Code (IaC): Comfort using Terraform or similar tools to manage deployment environments.
• Enterprise Ecosystems: Experience integrating platforms like Salesforce, HubSpot, ServiceNow, Zendesk, or Snowflake.
• Identity & Security: Familiarity with enterprise authentication systems (OAuth, SAML, SCIM, Okta) as they relate to autonomous tool-use.
• Solution Consulting Background: Experience in technical delivery or systems integration where understanding the "business case" was as critical as the code.
• Event-Driven Architectures: Experience with messaging platforms or streaming data pipelines that can feed real-time context to agents.

We keep our process focused and respectful of your time. Most candidates complete it in 2–3 weeks. Here’s what to expect:

1. Intro Call – 30 minutes
2. Culture & Values Alignment interview – Focused on your motivations to join InteractiveAI
3. Engineering Interview – Practical problem-solving and implementation-focused technical discussion
4. Delivery & Collaboration Interview – Working style, client interaction, communication
5. Offer – Final conversation and offer details