AI Engineer Manager

As part of this role, you will be responsible for: - Leading AI project delivery end to end, ensuring clear governance, strong stakeholder communication, and reliable execution. - Designing and building robust RAG systems, agentic frameworks, and LLM-powered solutions suitable for production environments. - Applying advanced prompt engineering techniques, including instruction design, few-shot prompting, structured outputs, and tool/agent prompts. - Leading feasibility assessments to determine the right technical approach, including prompting, RAG, fine-tuning, classical ML, or hybrid solutions. - Designing evaluation frameworks for AI systems, including LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go gates. - Running structured experiments across prompts, retrievers, chunking strategies, embeddings, reranking approaches, and models. - Identifying and categorizing model failures such as hallucinations, retrieval misses, instruction-following errors, and quality regressions. - Building scalable inference infrastructure and CI/CD pipelines for AI and ML models. - Automating the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, retraining, and continuous improvement. - Designing APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and scalability. - Mentoring junior engineers and contributing to proposals, solution design, and new business initiatives. The ideal candidate should have: - 6+ years of experience building and deploying AI, ML, or data-driven solutions in production environments. - Strong expertise in Python and solid Git practices. - Hands-on experience with LLM-powered solutions, RAG systems, and modern GenAI development patterns. - Practical experience with RAG components, including chunking, embeddings, retrieval, reranking, and evaluation. - Strong understanding of prompt engineering techniques, including structured outputs, few-shot prompting, instruction design, and tool/agent prompts. - Proven experience designing evaluation strategies for AI systems, including metrics, dataset curation, structured experimentation, and quality gates. - Experience with MLOps/LLMOps practices and tools such as MLflow, Weights & Biases, or similar platforms. - Solid cloud experience with AWS, Azure, or GCP. Azure experience is preferred. - Experience with containerization, orchestration, scalable inference, APIs, and microservices. - Understanding of event-driven architectures and production-grade engineering practices. - Ability to communicate clearly with engineering teams, senior stakeholders, and clients. - A pragmatic approach to AI delivery, balancing innovation, reliability, cost, latency, and business value. Nice to have: - Experience with Databricks MLOps platform. - Experience with LLM fine-tuning. - Experience building agentic GenAI systems. - Experience with Infrastructure as Code. - Knowledge of security and observability practices for AI services. - Background in classical machine learning. - Open-source contributions or public technical work. What about languages? Advanced English level is required for written and verbal communication. How much experience must I have? At least 6 years of professional experience building and deploying AI, ML, or software solutions in production environments. Our perks and benefits: 🍔 Every day lunches! (headquarters): - Vegetarian, vegan, gluten and sugar free options. - Gourmet meals every Friday with our on-site chef! ⚖️ Flexible working options to help you strike the right balance. 👨🏽‍💻 All the equipment you need to harness your talent (Macbook and accessories). ☕ Snacks and beverages available everyday (headquarters). 🎮 After office events, football, tennis and game nights (headquarters). ⚽️ Everyone is welcome to join our football league every Wednesday’s and Friday’s. Challenge your teammates to a pool game and win the office’s trophy! Tennis co