Senior Full Stack AI/ML Engineer

Our requirements

• 7+ years of software engineering experience, including 3+ years delivering production AI/ML pipelines or applications.
• Successfully delivered 2+ end-to-end AI solutions, including at least one enterprise conversational AI system deployed to production.
• Expert-level Python and strong understanding of software patterns, automated testing, and ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Hands-on experience with containerization and orchestration (Docker, Kubernetes), and automated deployments in cloud-native environments.
• Deep experience with multiple LLM families (e.g., GPT, Claude, Gemini, Llama, Mistral), prompt engineering, function-calling, workflow orchestration, and memory/state management.
• Proven experience delivering RAG systems (vector databases, hybrid search, embeddings, chunking, retrieval optimization).
• Working knowledge of agent-based frameworks: LangChain, LangGraph, Google ADK, plus familiarity with LlamaIndex, Semantic Kernel, AutoGen, CrewAI.
• MLOps/LLMOps tools (MLflow, Kubeflow), experiment tracking, compliance monitoring, model versioning, and automated evaluation frameworks (e.g., LangSmith, Ragas, DeepEval).
• Experience applying input/output guardrails, RBAC, PII masking, red-teaming, and secure AI controls.

Optional

• Prior experience building enterprise AI platforms or reusable AI accelerators.
• Familiarity with financial services regulatory environments and compliance workflows.

Your responsibilities

• Collaborate with stakeholders to translate business requirements into innovative AI solutions, maintaining compliance with security and governance standards.
• Design and deliver production-grade AI/ML systems, including conversational AI, chatbots, agentic AI workflows, and RAG-powered applications.
• Build secure and scalable cloud-native architectures, leveraging platforms like AWS, GCP or Azure.
• Develop APIs, backend microservices, frontend components, and enterprise system integrations.
• Implement MLOps/LLMOps practices: monitoring, evaluation, model versioning, rollback automation, and guardrails.
• Optimize latency, reliability, and availability for real-time applications, ensuring <5s response time under expected workloads.
• Ensure compliance with AI governance, security measures, and data privacy regulations including input/output guardrails and adversarial robustness.
• Note: Detailed project information will be shared during the recruitment process.

About the project

This is how we organize our work

This is how we work on a project

• Continuous Deployment
• Continuous Integration

What we offer

• Flexible cooperation model – choose the form that suits you best (B2B, employment contract, etc.)
• Hybrid work setup – 2 days from the office per week
• Collaborative team culture – work alongside experienced professionals eager to share knowledge
• Continuous development – access to training platforms and growth opportunities
• Comprehensive benefits – including Interpolska Health Care, Multisport card, Warta Insurance, and more
• High quality equipment – laptop and essential software provided

Benefits

• sharing the costs of sports activities
• private medical care
• sharing the costs of professional training & courses
• life insurance

Mindbox Sp. z o.o.