The Role
Build production-grade Generative AI applications, LLM-powered agents, reusable AI platforms, and developer tooling. Responsibilities include RAG, prompt and context engineering, tool orchestration, evaluation, guardrails, observability, secure AI development, Python-based APIs and microservices, cloud-native infrastructure, Kubernetes, CI/CD, testing, and Snowflake data engineering. The role also involves designing AI-ready data models, ingestion and transformation pipelines, secure data access, and integrations across enterprise applications.
Summary Generated by Built In
Ready to build the future of AI? Let's talk!
Are you passionate about Artificial Intelligence, obsessed with innovative technology and excited by the idea of turning complex challenges into smart, scalable solutions?
We're looking for an Expert AI Engineer / Full Stack Developer who knows how to take AI beyond experimentation and turn innovative ideas into real-world, production-ready solutions.
This isn't your typical development role! You'll be working at the forefront of Generative AI, LLMs, intelligent agents and enterprise data engineering, helping build reusable AI platforms and tools that empower development teams to work smarter, faster and more efficiently.
If Python is your playground, AI is your passion and building solutions that make a real impact is what gets you excited, this opportunity is for you!
Requirements
- AI Engineering.
- Expert knowledge of LLMs and Generative AI application engineering.
- Hands-on experience building production-grade LLM applications and AI agents.
- Strong understanding of Retrieval-Augmented Generation (RAG).
- Prompt and context engineering.
- Agent/tool integration and orchestration.
- Structured outputs and tool calling.
- Model and application evaluation.
- AI guardrails and human approval mechanisms.
- AI observability, reliability and production monitoring.
- Responsible and secure AI engineering.
- The source role already identifies agents, RAG, prompt/context engineering, tool integration, evaluation, guardrails and human approval gates as core AI engineering capabilities.
- AI Native Engineering
- Strong practical capability using AI throughout the software engineering lifecycle.
- Specification-driven development.
- AI-assisted implementation and refactoring.
- AI-assisted test generation and quality engineering.
- AI-supported code review and documentation.
- Ability to build reusable AI-enabled developer tooling and workflows.
- Strong understanding that AI-generated outputs still require accountable engineering review.
- The reference specification explicitly expects responsible AI-assisted development across specification, coding, review, testing, documentation and operations while retaining accountability for correctness and security.
- Software & Platform Engineering.
- Expert Python engineering.
- Strong API and microservice engineering.
- REST-based service integration.
- Event-driven and asynchronous architecture.
- Containers and Kubernetes.
- CI/CD and automated testing.
- Infrastructure as Code.
- Secure software development.
- Production observability and operational readiness.
- Strong architecture and systems-thinking capability.
- Python/Java, APIs, microservices, AWS/cloud-native engineering, Docker/Kubernetes/Terraform, automated testing, CI/CD and production readiness are all represented in the reference Expert specification.
- Snowflake & Data Engineering.
- Strong Snowflake engineering and implementation capability.
- Design and implementation of data models suitable for AI and application consumption.
- Advanced SQL.
- Data ingestion and transformation.
- ETL/ELT engineering.
- Metadata, lineage and data quality.
- Secure data access patterns.
- Integration between Snowflake, AI services, APIs and enterprise applications.
- Performance and cost-conscious data engineering.
Skills Required
- Expert knowledge of LLMs and Generative AI application engineering
- Hands-on experience building production-grade LLM applications and AI agents
- Strong understanding of Retrieval-Augmented Generation, prompt engineering, context engineering, structured outputs, and tool calling
- Experience with agent and tool integration, orchestration, model and application evaluation, AI guardrails, and human approval mechanisms
- Experience with AI observability, reliability, production monitoring, responsible AI, and secure AI engineering
- Practical use of AI throughout the software engineering lifecycle, including specification, implementation, refactoring, testing, code review, and documentation
- Ability to build reusable AI-enabled developer tooling and workflows
- Expert Python engineering
- Strong API, REST, microservice, event-driven, and asynchronous architecture experience
- Experience with containers, Kubernetes, CI/CD, automated testing, Infrastructure as Code, and secure software development
- Strong architecture, systems-thinking, production observability, and operational readiness capabilities
- Strong Snowflake engineering and implementation capability
- Experience designing AI- and application-ready data models, advanced SQL, data ingestion, transformation, and ETL/ELT pipelines
- Knowledge of metadata, lineage, data quality, secure data access, and integration between Snowflake, AI services, APIs, and enterprise applications
- Experience with performance- and cost-conscious data engineering
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The Company
What We Do
Sabenza IT is a niche recruitment company specializing in Information Technology, SAP, Finance, and Engineering roles, with over 23 years of experience.
