Senior / Lead Machine Learning Engineer (Ai & Llm Systems) - Hybrid (2 Days Office)

Senior / Lead Machine Learning Engineer (AI & LLM Systems) - Hybrid (2 days office)
Job Openings Senior / Lead Machine Learning Engineer (AI & LLM Systems) - Hybrid (2 days office)
About the job
ABOUT THE OPPORTUNITY
Join a leading global technology platform as a
Senior or Lead Machine Learning Engineer
and drive the design, development, and production deployment of advanced AI and Large Language Model (LLM) systems that power intelligent product features and workflows at scale.
Reporting to the Head of AI or VP Engineering, you'll shape the architecture of scalable machine learning solutions, guide technical direction across AI initiatives, and collaborate with world-class engineering, product, and research teams to deliver high-impact AI products. This role offers the perfect blend of technical leadership and hands-on engineering, where you'll architect and implement robust LLM-based systems, lead training and fine-tuning of large-scale models, and develop sophisticated workflows including retrieval-augmented generation (RAG), vector databases, and agentic AI components.
You'll have the opportunity to translate cutting-edge research into production-ready systems while mentoring engineers and driving best practices across the organization. Working at the intersection of research and engineering, you'll transform advanced AI concepts into scalable, reliable production systems that deliver measurable business value to millions of users worldwide.
Project & Context
We'll architect and build
production-grade machine learning and LLM systems
that power intelligent features, automation, and AI-driven workflows for a global technology platform serving millions of users. Your core responsibilities center on designing robust, scalable ML infrastructure, making critical architectural decisions about system design, model selection, deployment strategies, and technical approaches that balance innovation with production reliability, scalability, and performance requirements.
A significant portion of your work involves
leading training, fine-tuning, and optimization of large-scale language models
for real production use cases. This includes selecting appropriate base models, implementing fine-tuning strategies using domain-specific data, optimizing model performance for inference efficiency, and ensuring models meet quality, safety, and compliance requirements for production deployment. You'll develop and maintain sophisticated
AI workflows
including RAG systems that ground LLM outputs in accurate information, information retrieval systems, vector databases for semantic search, and agentic AI components that can reason, plan, and execute multi-step tasks autonomously.
Experimentation and validation
are critical - you'll drive comprehensive experimentation frameworks, design and execute benchmarks to evaluate model performance, and implement rigorous evaluation methodologies measuring both technical metrics (accuracy, latency, throughput) and business outcomes (user satisfaction, task completion). Cross-functional collaboration defines your daily work as you partner with engineering teams to integrate ML systems, work with product managers to translate business requirements into technical solutions, and collaborate with research teams to apply cutting-edge approaches to production problems.
Technical leadership and mentorship
are key expectations at the Senior/Lead level. You'll mentor engineers through code reviews, architecture reviews, and knowledge sharing, establish best practices for ML engineering including testing strategies and deployment patterns, and guide the team in model development, system design, and production ML practices. You'll translate research insights into production systems by evaluating emerging research, prototyping promising approaches, and implementing production-ready versions with clear success metrics.
The role demands expertise in modern ML tooling including vector databases (Pinecone, Weaviate, Chroma), embedding models, semantic search, model serving infrastructure, and ML observability tools. You'll make critical architectural decisions about fine-tuning versus few-shot prompting, efficient RAG retrieval, multi-step agentic workflow orchestration, and ensuring systems scale efficiently. Working in a hybrid environment with 2 days per week in the Lisbon office enables both focused individual work and collaborative team activities.
Core Tech Stack:
Python (primary), PyTorch (preferred) or TensorFlow, LLM frameworks (LangChain, LlamaIndex), vector databases, embedding models
ML Infrastructure:
Model training frameworks, experiment tracking, model serving platforms, feature stores, ML monitoring tools
Engineering Practices:
Production ML system design, model evaluation, A/B testing, monitoring and observability, scalable deployment
Scale:
Global platform serving millio