Artificial Intelligence Engineer

Job Title: AI Engineer

Location: Kraków, Poland

Work Model: Hybrid 2 times per week in office

Employment Type: B2B

Role Overview

We are looking for a strong Full Stack AI Engineer to build and ship end-to-end AI-powered products. The role involves owning the complete delivery lifecycle—from data/API integration and model enablement through to secure, scalable user-facing applications.

The initial engagement will focus on developing a reusable best-practice and skill-inventory platform that can be leveraged across multiple teams and initiatives.

Key Responsibilities

• Design, build, and maintain full-stack AI applications including web UIs, backend services, and AI/ML components.
• Develop and operate LLM-enabled services using prompting, RAG, tool/function calling, evaluation frameworks, and guardrails.
• Build APIs and microservices that integrate AI models with enterprise systems and data sources.
• Implement data ingestion, transformation, indexing, and retrieval pipelines, including vector-store workflows.
• Create responsive front-end experiences such as AI chat interfaces, copilots, workflow assistants, and dashboards.
• Establish MLOps / LLMOps practices including CI/CD, model versioning, monitoring, and quality checks.
• Define and execute testing strategies including unit, integration, offline evaluation, red-teaming, and performance testing.
• Optimize solutions for latency, cost, scalability, and reliability using caching, batching, asynchronous processing, and observability tooling.
• Ensure security, privacy, and compliance-by-design through access controls, secure data handling, and auditability.
• Collaborate with product, UX, data, and risk stakeholders to deliver measurable business outcomes.

Required Skills & Experience

• Strong software engineering background with hands-on full-stack delivery experience.
• Backend: Python and/or Java/Node.js, REST/GraphQL APIs, microservices, and event-driven architectures.
• Frontend: React/TypeScript (or similar modern framework), component-based architecture, and state management.
• AI/ML: Practical experience with LLMs, embeddings, RAG architectures, and evaluation techniques.
• Data: SQL plus experience with Elasticsearch/OpenSearch, vector databases, document stores, or graph databases.
• Cloud & Containers: Docker, Kubernetes, and at least one major cloud platform (Azure, AWS, or GCP).
• DevOps: CI/CD, Infrastructure as Code (e.g., Terraform), monitoring, logging, and observability tools (e.g., OpenTelemetry).
• Knowledge of secure coding practices, OWASP principles, and authentication/authorization patterns.

Nice to Have

• Experience with agentic workflows, orchestration frameworks, and tool integration.
• Understanding of model governance, explainability, and responsible AI controls.
• Experience building internal developer platforms or reusable AI components.
• Experience working in regulated environments such as financial services, healthcare, or insurance.