ai engineer.

You will join a forward-thinking, technically ambitious environment where your code directly automates high-impact enterprise operations. In return, you will receive:

• A competitive compensation package commensurate with experience.
• Hybrid working model
• Private health insurance
• The opportunity to work on cutting-edge, production-grade LLM architectures, agentic pipelines, and cloud ecosystems.
• A collaborative engineering culture that promotes technical ownership, continuous learning, and career advancement.

As an AI Engineer, you will design, deploy, and refine robust AI workflows integrated seamlessly with enterprise core systems. Your key responsibilities will include:

• LLM & Agentic Workflows: Build autonomous agentic architectures and LLM workflows tailored to automate multifaceted business operations.
• Backend & Orchestration: Develop high-performance backend microservices in Python (FastAPI) and orchestrate multi-system data flows using workflow platforms such as n8n.
• Prompt Engineering & Reliability: Design optimized prompts, define structured outputs, implement resilient fallback logic, and continuously fine-tune accuracy through iterative UAT cycles.
• Cloud Deployment & DevOps: Deploy scalable solutions across Azure (Azure Container Apps, Kubernetes) leveraging Azure Repos and Azure DevOps Pipelines.
• Production Engineering: Proactively identify and resolve production challenges, including timeouts, concurrency bottlenecks, and API latency.
• Technical Guidance: Author solution design documentation, deliver accurate effort estimates, and mentor/support junior engineers within the team.

To excel in this position, you should combine strong Python software engineering practices with hands-on experience shipping real-world AI applications to production. We are looking for:

• Experience: 2–5 years of professional engineering experience with a demonstrable track record of delivering AI applications into production.
• Programming & LLM Stack: Strong proficiency in Python (FastAPI) along with hands-on experience using Azure OpenAI (or comparable LLM APIs), Retrieval-Augmented Generation (RAG), and agentic design patterns.
• Frameworks & Orchestration: Practical familiarity with orchestration frameworks such as LangChain / LangGraph and automation/workflow tools like n8n.
• Core Engineering Fundamentals: Solid command of RESTful APIs, SQL databases, Git version control, and CI/CD pipelines.
• Languages & Communication: Fluent English communication skills with the ability to articulate technical concepts to cross-functional teams.

While not mandatory, experience in any of the following areas will be considered an asset:

• Integration experience with Salesforce or Enterprise RPA platforms.
• Hands-on exposure to intelligent document processing (OCR, automated document parsing, handling complex attachments).
• Domain experience within the banking, fintech, or insurance industries.

2-5 years of experience in AI Engineering