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