Artificial Intelligence Engineer

Hi! We are Skilld by eJobs, and we are currently recruiting for a hands-on AI Engineer at Mid–Senior level to join the engineering team of our client and contribute directly to the design, development, and deployment of AI-powered applications.

This role is focused on hands-on engineering execution, with a strong emphasis on agentic AI, Databricks-based knowledge solutions, AI application development, and cloud integration.

Key Responsibilities:

• Design, build, test, and continuously improve AI agents and agentic workflows.
• Develop orchestration workflows using LangChain, LangGraph, and DeepAgents.
• Use MLflow for experiment tracking, evaluation, and production-oriented AI development.
• Evaluate agent performance, reliability, and output quality using systematic testing and evaluation approaches.
• Translate business and technical requirements into practical, production-ready AI solutions.
• Architect and implement AI-enabled knowledge bases on Databricks.
• Build end-to-end data pipelines covering ingestion, parsing, extraction, transformation, and automation.
• Leverage Databricks AI Functions and related platform capabilities to enable AI-powered data workflows.
• Design and implement Retrieval-Augmented Generation (RAG) architectures.
• Work with PySpark and Databricks SDKs to integrate data and AI capabilities into applications
• Build user-facing AI applications and prototypes using Streamlit.
• Integrate applications with AI agents, knowledge bases, APIs, and backend services.
• Implement custom user management and authentication where required.
• Rapidly prototype solutions while maintaining sound software engineering practices and a clear path toward production readiness.
• Design and implement simple, efficient application databases to support AI-powered tools.
• Develop backend components and data access layers required by AI applications.
• Ensure solutions are maintainable, reliable, and appropriately designed for their intended scale.
• Deploy AI applications and supporting infrastructure using Azure Cloud Services.
• Integrate AI applications with Azure-based services securely and efficiently.
• Apply appropriate cloud architecture, security, and scalability practices for AI workloads.

Technical Requirements:

• Strong proficiency in Python and SQL .
• Solid software engineering fundamentals, including clean code, testing, debugging, and version control.
• Experience developing production-oriented applications rather than exclusively research or proof-of-concept solutions.
• Hands-on experience with LangChain and LangGraph .
• Experience with DeepAgents and/or comparable agentic AI frameworks.
• Practical experience with MLflow .
• Strong understanding of LLM-based application architectures, agentic workflows, and AI evaluation.
• Strong hands-on experience with Databricks .
• Proficiency with PySpark .
• Experience with Databricks SDKs for ML/AI .
• Strong understanding of RAG architectures , knowledge bases, embeddings, retrieval, and AI-enabled data pipelines.
• Experience with data ingestion, parsing, extraction, transformation, and automation workflows.
• Solid understanding of Microsoft Azure , particularly in the context of deploying and supporting AI applications.
• Experience integrating cloud services into application architectures.

Working Style

• Strong commitment to Test-Driven Development (TDD) and automated testing.
• Ability to write robust, maintainable, production-ready code.
• Comfortable using modern AI coding assistants, such as GitHub Copilot , to accelerate development while maintaining high engineering and code-quality standards.
• Strong problem-solving skills and a pragmatic approach to building and iterating on AI solutions.
• Ability to work independently and take ownership of technical deliverables.
• Comfortable working as an external contributor while integrating effectively with internal engineering teams, standards, processes, and delivery rhythms.
• Strong communication skills and the ability to explain technical decisions clearly to both technical and non-technical stakeholders.

Nice to Have

• Experience deploying AI/LLM applications into production.
• Experience with enterprise-grade authentication and user-management solutions.
• Experience with Azure AI services or other Azure-native AI capabilities.
• Experience designing evaluation frameworks for LLMs and AI agents.
• Experience with CI/CD and modern software delivery practices.
• Familiarity with vector databases, search technologies, and advanced RAG patterns.
• Experience working in environments where AI applications need to meet enterprise security and governance requirements.

We offer:

• A modern environment with flexible hours and hybrid ways of working.
• Private medical insurance
• Meal Vouchers