AI Engineer

About the Role & Team
We are seeking a Forward-Deployed AI Architect to help our enterprise customers radically accelerate and modernize their software delivery lifecycle. In this elite, high-impact role, you will embed directly with customer engineering teams to prove the value of AI-led engineering through hands-on architecture, agentic workflows, and measurable delivery outcomes.
This role is designed for senior architects and builders who sit at the intersection of core enterprise software architecture and cutting-edge AI engineering. You will help customers transition from traditional, ticket-bound Scrum cycles toward intent-driven, increasingly autonomous delivery models where AI tools, agents, and human governance seamlessly turn business intent into production-ready software.


Candidate Profile: Qualifications & Experience
Must-Have Attributes
- Experience: 15+ years of proven experience across software engineering, architecture, product/platform engineering, or solution architecture, backed by a strong track record of product-side delivery.
- SDLC Mastery: Deep, foundational understanding of enterprise software architecture, DevSecOps pipelines, Agile/Scrum frameworks, and the broader product lifecycle.
- Hands-on AI Fluency: Direct, practical fluency with AI development tools, IDE-based AI assistants, agentic workflows, and AI-enabled software delivery. You must possess the ability to build, evaluate, and deeply explain working AI solutions.
- Modern AI Tech Stack: Experience leveraging tools and frameworks such as Claude Code, Cursor, Windsurf, GitHub Copilot, LangChain, AutoGen, LlamaIndex, Semantic Kernel, MCP (Model Context Protocol), RAG, and vector databases.
- Hands on in Retrieval Augmented Generation (RAG), Vector Databases, Evaluation Frameworks, Prompt Engineering, Tool Calling, Function Calling, Multi-agent orchestration, AI Observability

Good-to-Have Skills
- Customer-Facing Excellence: Prior experience in high-touch, customer-facing technical roles (e.g., Forward-Deployed Engineer, Principal Solutions Architect, Field Engineer, or Pre-Sales Engineering Leader).
- AI Governance & Security: A strong grasp of AI governance models, human-in-the-loop controls, responsible AI frameworks, observability, and production readiness for AI-enabled enterprise systems.

Job Responsibilities

Key Responsibilities
1. Client Engagement & Strategic Architecture

- Identify AI Opportunities: Work directly with customer engineering, product, architecture, and executive teams to diagnose SDLC bottlenecks and map out high-value areas for AI-led acceleration.
- Design & Build Proofs of Concept: Act as a hands-on builder to design, develop, and deliver working proofs utilizing modern AI engineering tools, agentic workflows, and automation frameworks.
- Lead the Intent-Driven Transition: Guide clients away from rigid, manual sprint mechanics toward fluid, intent-driven delivery loops capable of autonomously generating plans, code, tests, documentation, and validation artifacts.

2. Value Measurement & Executive Alignment

- Quantify AI Impact: Define, measure, and report on hard engineering acceleration outcomes, including reduced cycle times, faster story-to-code conversion, improved automated testing, and accelerated code reviews.
- Technical & Executive Presentation: Communicate complex technical approaches, agentic architectures, proof results, and long-term adoption strategies to both engineering teams and C-level stakeholders.