Ai Implementation Engineer

Role:

AI Implementation Engineer Location:

Hybrid / Remote (Preference: Hybrid) Employment Type:

Full-Time / Contract (based on hiring model) Start Date:

Immediate / ASAP Contract Rate:

Dependant On Experience

Role Summary My client are seeking a highly capable AI Implementation Engineer to design, build, integrate, deploy, and operate production-grade AI agents embedded into enterprise workflows. This role is ideal for an engineer who thrives in building real-world AI systems, not prototypes, and who is comfortable delivering robust solutions with strong engineering discipline. The engineer will be responsible for developing AI agents using pro-code frameworks, implementing approved agent logic and tool workflows, integrating with enterprise systems using APIs and event-driven architectures, and deploying solutions into secure production environments. This position is delivery-driven, requiring strong software engineering fundamentals, excellent Python capability, and practical experience with agentic AI systems, vector databases, and production-grade integration patterns.

Key Responsibilities The AI Implementation Engineer will be responsible for building and deploying production-grade AI agents using Python frameworks, implementing agent logic, tools, orchestration workflows, and execution flows based on approved functional and technical specifications. The role will include integrating AI agents into enterprise systems and workflows through REST APIs, webhooks, and event-driven integration patterns, enabling secure task execution, approvals, notifications, and automated updates across business platforms. The engineer will deploy and operate AI solutions in secure enterprise environments, collaborate with platform, DevOps, and architecture teams to ensure governance and reliability, and participate in structured testing, release, and iteration cycles. The engineer will also continuously tune, optimise, and enhance deployed AI agents based on real user feedback, monitoring insights, and operational performance to ensure measurable production impact.

Must-Have Qualifications • Bachelor’s degree in Computer Science, Engineering, Business Administration, or a related field (Master’s degree preferred). • 4–6+ years of experience in software engineering, AI implementation, or solutions engineering. • 4–5+ years of strong experience in Python. • Hands-on experience building agent-based systems using Microsoft agent frameworks or comparable agentic frameworks. • Solid understanding of vector databases, embeddings, and AI search/retrieval patterns (RAG, hybrid retrieval). • Experience developing integrations using REST APIs, webhooks, and event-driven workflows. • Strong problem-solving skills and attention to production quality. • Ability to work effectively in cross-functional engineering environments. Tools Required Qualifications • FastAPI • LangChain (agent tools, chains, RAG patterns), LangGraph (multi-step agent workflows, state machines) • Semantic Kernel (Python SDK) (Microsoft-first agent framework) • OpenAI / Azure OpenAI SDK • PydanticAI (structured agent outputs) • Azure AI Search SDK

Preferred / Nice-to-Have Qualifications • Demonstrated experience building AI-enabled or agent-based services using Microsoft AI Foundry, Azure AI Studio extensibility, or equivalent frameworks. • Strong API integration experience across enterprise systems. • Experience deploying and operating solutions in secure, enterprise-grade environments. • Ability to work against structured backlogs and clearly defined specifications. • Knowledge of MLflow, Docker, Hugging Face Transformers

Work Environment & Collaboration The role will involve close collaboration with product teams, architecture teams, platform/DevOps teams, and quality/testing teams to ensure AI capabilities are delivered with production quality and real operational impact.

Core Competencies We Will Evaluate • Python engineering quality and production coding practices. • AI agent development and orchestration capability. • Retrieval design patterns (vector DB + embeddings + AI search). • Integration engineering (REST, webhooks, event-driven workflows). • Debugging and operational mindset. • Ability to deliver against structured requirements and enter