AI Engineering Manager

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit

blend360. com We are seeking an AI Engineering Manager to contribute to our next level of growth and expansion. Leadership and Delivery - Lead project delivery end to end, with clear governance, stakeholder communication, and accountability for outcomes - Build and mentor a high-performing AI engineering team, establishing technical standards and fostering a culture of quality and pragmatism - Own proposals and new business initiatives, defining technical feasibility and communicating risks and tradeoffs clearly to clients - Define what AI systems should and should not attempt, setting realistic expectations and being upfront about limitations - Conduct technical reviews and architectural assessments to maintain high standards across projects and team AI Development - Guide the design and delivery of RAG systems, agentic frameworks, and LLM-powered solutions that are robust enough for production - Lead the application of advanced prompt engineering techniques including instruction design, few-shot sets, structured outputs, and tool/agent prompts - Run feasibility assessments to choose the right approach for each problem: prompting, RAG, fine-tuning, or classical ML - Mentor engineers on end-to-end AI system design and production deployment practices Evaluation and Quality - Design evaluation frameworks including LLM-as-a-judge approaches, metric creation (Ver el correo electrónico en remoshift.com, Ver el correo electrónico en remoshift.com), and go/no-go gates - Lead structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence not intuition - Establish team practices for identifying and categorising model failures including hallucinations, retrieval misses, and instruction-following errors - Set quality standards that ensure AI systems meet production reliability requirements MLOps and Infrastructure - Build scalable inference infrastructure and CI/CD pipelines for AI/ML models that support rapid iteration and reliable deployment - Automate the full MLOps/LLMOps lifecycle: tracking, versioning, deployment, monitoring, and retraining across the team - Design APIs, microservices, and orchestration layers optimised for latency, cost, and reliability - Lead infrastructure decisions that balance technical excellence with business efficiency What We Are Looking For - 7+ years building and deploying AI solutions in production environments - 2+ years of direct team leadership or technical management experience - Expert Python proficiency, strong Git practices, and experience with ML/LLM versioning and deployment - Solid cloud experience across AWS, Azure, or GCP—preference for Azure—plus containerisation and orchestration knowledge - Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation - Proven MLOps/LLMOps track record using tools like MLflow, Weights and Biases, or similar - Practical evaluation design skills: metrics, dataset curation, and structured experimentation - Experience with event-driven architectures, APIs, and microservices - A clear communicator equally comfortable with engineering teams and senior stakeholders - Strong hiring and team-building instincts with proven mentoring experience What about languages? - English: Advanced (required for effective communication with global teams and client leadership). How much experience must I have?