Engineering Tech Lead

Mindbox connects top IT talents with technology projects for leading enterprises across Europe. The company focuses on consulting, engineering, and talent to build meaningful digital solutions.

• Strong proficiency in Java (Spring Boot) for REST APIs, asynchronous/event-driven services, security, and performance tuning.
• Advanced front-end experience with React, API integration, and modern UI patterns.
• Database expertise in PostgreSQL and in-memory databases like Redis.
• Experience with streaming/messaging platforms, preferably Kafka.
• Proven hands-on experience with production LLM applications beyond prototypes.
• Deep understanding of tokenization, context windows, temperature adjustments, and model limitations across major LLM providers.
• Strong capability in designing structured prompts for function/tool calling, generating structured JSON, and handling multi-turn conversations.
• Experience with Kubernetes including deployments, services, RBAC, Helm, Kustomize, autoscaling, and observability.
• Proficiency with CI/CD tools like GitHub Actions, GitLab, and Jenkins.
• Infrastructure as Code experience with Terraform.
• Cloud Infrastructure experience with AWS or GCP.
• Familiarity with LangChain, OpenAI/Azure OpenAI, Anthropic, or comparable LLM ecosystems.
• Understanding of model benchmark testing and risk classification in AI workflows.
• Knowledge of secure API/LLM gateway design.

• Design, build, and deliver production-quality services ensuring compliance with enterprise controls and security standards.
• Enhance and optimize DecOps pipelines including CI/CD automation, testing frameworks, and system observability.
• Architect and maintain robust systems for high traffic and fault tolerance with efficient resource allocation.
• Design and iterate on prompts for diverse LLM tasks such as instruction-following, structured outputs, summarisation, reasoning, classification, and code generation.
• Manage prompt libraries with version control treating prompts as engineering artifacts.
• Implement evaluation pipelines for prompt performance using frameworks like PromptFlow and LangSmith.
• Architect and implement autonomous agent systems, multi-agent workflows, tool calling orchestration, and planning loops with memory-augmented reasoning.
• Integrate LLM observability including logging, tracing, evaluation, and automated guardrails to ensure production quality.
• Advise on Responsible AI deployment including output validation, human-in-the-loop strategies, and risk mitigation.
• Build and optimize Kubernetes deployments (Helm, Kustomize), implement autoscaling, RBAC, and secure ingress.
• Maintain and improve CI/CD pipelines using GitHub Actions, GitLab CI, and Jenkins.
• Apply Infrastructure as Code with Terraform deploying across AWS or GCP environments.