Senior Full Stack AI/ML Engineer

Mindbox is a tech-driven company connecting top IT talents with technology projects for leading enterprises across Europe. The role is part of a major data transformation program delivering business-critical insights, conversational AI systems, agent-based workflows, and advanced analytics solutions.

• 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.
• Experience with 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.

Nice to have:

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

• 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.