Lead ML/AI Platform Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead ML/AI Platform Engineer based in Spain. As a Lead ML/AI Platform Engineer, you will own the infrastructure that moves machine learning and AI from experimentation into reliable production systems. You will define technical direction for ML/AI architecture, tooling, standards, and platform strategy while partnering closely with data science, DevOps, backend engineering, product, and leadership teams. The role spans managed AWS machine learning services, open-source ML tooling, model serving, inference pipelines, and production integrations. You will also drive the engineering strategy for GenAI, LLMs, retrieval architectures, and agentic workflows. Your decisions will establish the foundation for future AI-powered products in a highly regulated financial technology environment. This is a fully remote independent contractor opportunity within a globally distributed team, with reliable overlap with US Pacific business hours. Own the ML/AI platform: Lead the architecture and operation of training infrastructure, model serving, inference pipelines, model registries, feature pipelines, and production integrations. Set technical direction: Partner with the Data Platform Architect to define ML/AI architecture, engineering standards, tooling strategies, and build-versus-buy decisions. Build production ML systems: Oversee feature engineering, model training, tuning, registration, deployment, monitoring, and hosted inference using AWS managed services and open-source tooling. Drive GenAI and LLM engineering: Develop practical strategies for RAG, prompt engineering, evaluation, fine-tuning, model serving, and agentic workflows while balancing cost, latency, quality, and safety. Develop agentic capabilities: Evaluate and implement emerging agent technologies and workflow patterns, including MCP, stateless and stateful architectures, and appropriate guardrails for regulated environments. Own the serving layer: Design scalable ML services and API contracts that integrate cleanly with Java microservices, making informed decisions around performance, latency, throughput, and reliability. Enable research-to-production: Partner with Data Science to productionize models and create the infrastructure, pipelines, and deployment processes required to move experimentation into reliable production. Establish MLOps practices: Drive model monitoring, drift detection, reproducibility, experiment tracking, model registries, and cost observability in collaboration with DevOps. Shape the ML roadmap: Work with product, data, and engineering leadership to identify high-impact AI/ML opportunities and translate them into actionable technical roadmaps. Provide technical leadership: Mentor senior engineers, guide complex architectural decisions, and represent the AI/ML function in cross-functional discussions. Communicate technical strategy: Translate complex architectural trade-offs into clear design documents and executive-level recommendations for both technical and non-technical stakeholders. Requirements: Senior engineering experience: 8+ years in software or ML engineering, including at least 5 years delivering production machine learning systems and owning complex, ambiguous problems end to end. Technical leadership: Demonstrated experience shaping ML strategy, mentoring senior engineers, and serving as a trusted decision-maker for challenging architectural problems. AWS ML expertise: Hands-on experience with Amazon SageMaker for training, tuning, hosted endpoints, and model registry, as well as Amazon Bedrock and AgentCore for GenAI and agentic applications. Open-source ML tooling: Practical experience with JupyterLab, Spark, MLflow, and related machine learning development and experimentation tools. AWS platform knowledge: Deep familiarity with services including S3, Athena, Redshift, Glue, Step Functions, and Lambda, com