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AI/ML Engineer Lead
Responsibilities:
* Lead and mentor the AI/ML engineering team, fostering a culture of technical excellence, continuous learning, and collaboration.
* Define and communicate the technical vision and roadmap for AI/ML initiatives across the organization.
* Conduct code reviews and architectural reviews to ensure best practices, scalability, and security in AI/ML systems.
* Actively design, build, and deploy LLM-powered applications and internal tools for use cases such as document understanding, process automation, and knowledge retrieval.
* Develop and maintain reusable AI infrastructure and platform components that support application teams (e.g., retrieval-augmented generation, prompt management, model orchestration).
* Oversee the end-to-end lifecycle of ML models: experimentation, evaluation, versioning, and deployment using tools like MLflow and CI/CD pipelines.
* Work with embeddings, vector stores, and similarity search to enable contextual AI responses in production systems.
* Integrate with vector databases (e.g., FAISS, Weaviate, or Pinecone) to support semantic search and information retrieval at scale.
* Collaborate with Data Engineers, Backend Engineers, and other stakeholders to align AI/ML work with business objectives and technical constraints.
* Champion good engineering practices around testing, observability, monitoring, and CI/CD for AI components.
* Guide technical hiring, conduct candidate interviews, and contribute to building a diverse and inclusive team.
* Lead discussions on AI ethics, responsible usage, model explainability, and compliance – especially for regulated use cases in healthcare.
* Contribute to and drive architectural decisions, system design, and strategic product discussions.
* Support team members through career development, performance feedback, and professional growth opportunities.
Requirements – Must have:
* 5+ years of hands-on experience in AI/ML engineering, with at least 2+ years in a leadership, mentorship, or senior engineering role.
* Proven track record of successfully building and deploying LLM-based systems in production (e.g., GPT, Claude, LLaMA, Mistral, etc.).
* Strong understanding of modern ML infrastructure: experiment tracking, model versioning, and deployment orchestration (MLflow, Weights & Biases, DVC, etc.).
* Hands-on experience with embeddings, vector databases (e.g., FAISS, Weaviate, Pinecone), and semantic search implementations.
* Solid grasp of AI/ML fundamentals: model evaluation, hyperparameter tuning, feature engineering, and performance optimization.
* Demonstrated experience leading technical teams, including hiring, mentoring, performance management, and conflict resolution.
* Experience driving technical strategy, architectural decisions, and cross-functional collaboration with non-technical stakeholders.
* Awareness of AI ethics, responsible AI practices, bias mitigation, and model explainability concerns.
* Strong communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences.
* Team-first mindset and experience fostering collaborative, agile environments (Scrum or Kanban).
* Professional working proficiency in English.
Requirements – Nice to have:
* Experience with Docker, Kubernetes, and containerization/orchestration in production environments.
* Expertise in prompt engineering, retrieval-augmented generation (RAG), and frameworks like LangChain or LlamaIndex.
* Experience with cloud infrastructure and MLOps across Azure, AWS, or GCP; cloud-agnostic or multi-cloud experience.
* Background in regulated industries (life sciences, healthcare), especially GxP environments and compliance requirements.
* Familiarity with advanced monitoring, logging, and observability tools for AI/ML workloads in production.
* Experience with distributed systems, big data processing, or complex data pipelines.
* Track record of contributing to open-source projects or publishing research in AI/ML domains.
* Spanish and/or Catalan language skills.
* Languages: English B2/C1