AI ENGINEERING MANAGER

<p><strong>Leadership and Delivery</strong></p><ul><li>Lead project delivery end to end, with clear governance, stakeholder communication, and accountability for outcomes</li><li>Build and mentor a high-performing AI engineering team, establishing technical standards and fostering a culture of quality and pragmatism</li><li>Own proposals and new business initiatives, defining technical feasibility and communicating risks and tradeoffs clearly to clients</li><li>Define what AI systems should and should not attempt, setting realistic expectations and being upfront about limitations</li><li>Conduct technical reviews and architectural assessments to maintain high standards across projects and team</li></ul><p><strong>AI Development</strong></p><ul><li>Guide the design and delivery of RAG systems, agentic frameworks, and LLM-powered solutions that are robust enough for production</li><li>Lead the application of advanced prompt engineering techniques including instruction design, few-shot sets, structured outputs, and tool/agent prompts</li><li>Run feasibility assessments to choose the right approach for each problem: prompting, RAG, fine-tuning, or classical ML</li><li>Mentor engineers on end-to-end AI system design and production deployment practices</li></ul><p><strong>Evaluation and Quality</strong></p><ul><li>Design evaluation frameworks including LLM-as-a-judge approaches, metric creation (recall@k, precision@k), and go/no-go gates</li><li>Lead structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence not intuition</li><li>Establish team practices for identifying and categorising model failures including hallucinations, retrieval misses, and instruction-following errors</li><li>Set quality standards that ensure AI systems meet production reliability requirements</li></ul><p><strong>MLOps and Infrastructure</strong></p><ul><li>Build scalable inference infrastructure and CI/CD pipelines for AI/ML models that support rapid iteration and reliable deployment</li><li>Automate the full MLOps/LLMOps lifecycle: tracking, versioning, deployment, monitoring, and retraining across the team</li><li>Design APIs, microservices, and orchestration layers optimised for latency, cost, and reliability</li><li>Lead infrastructure decisions that balance technical excellence with business efficiency</li></ul> <p><strong>What We Are Looking For</strong></p><ul><li>7+ years building and deploying AI solutions in production environments</li><li>2+ years of direct team leadership or technical management experience</li><li>Expert Python proficiency, strong Git practices, and experience with ML/LLM versioning and deployment</li><li>Solid cloud experience across AWS, Azure, or GCP—preference for Azure—plus containerisation and orchestration knowledge</li><li>Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation</li><li>Proven MLOps/LLMOps track record using tools like MLflow, Weights and Biases, or similar</li><li>Practical evaluation design skills: metrics, dataset curation, and structured experimentation</li><li>Experience with event-driven architectures, APIs, and microservices</li><li>A clear communicator equally comfortable with engineering teams and senior stakeholders</li><li>Strong hiring and team-building instincts with proven mentoring experience</li></ul><p><strong>What about languages?</strong></p><ul><li>English: Advanced (required for effective communication with global teams and client leadership).</li></ul><p><strong>How much experience must I have?</strong></p><p>7+ years of hands-on AI/ML engineering experience in production environments, with 2+ years of direct team leadership or technical management responsibility.</p><p><strong>Nice to Have</strong></p><ul><li>Databricks MLOps platform</li><li>LLM fine-tuning experience</li><li>Building agentic GenAI systems</li><li>Infrastructure as Code</li><li>Security and observability for AI services</li><li>Classical ML background</li><li>Open-source contributions</li></ul> <p><strong>Our Perks and Benefits:</strong></p><p>🏥 Health and Well-being:</p><ul><li>At-home medical assistance via EMI (or similar provider) through Asobursatil, available for all employees from AllStar to Analyst level.</li><li>Private healthcare plans for Lead-level roles and above.</li></ul><p>🎉 Celebrations and Recognitions:</p><ul><li>Christmas kit delivered to all employees.</li><li>1 day off for academic graduation.</li><li>Family Day: 1 day off every semester (must be taken within the same semester).</li></ul><p>💰 Financial Health and Savings (Work Together, Get Together Program):</p><ul><li>Savings incentive program via Asobursatil:<ul><li>Year 1: Blend contributes 50% of your monthly savings.</li><li>Year 2: Blend contributes 100% of your monthly savings.</li><li>Year 3+: Blend contributes 150% of your monthly savings.</li></ul></li><li>Savings can be withdrawn in July and December.</li></ul><p>📚 Educational Loans and Subsidies:</p><ul><li>Forgivable education loans subject to committee approval and budget availability.</li><li>Requirements: 1+ year at Blend, no disciplinary actions in the past 6 months, successful completion of prior training, and knowledge sharing within 6 months post-training.</li><li>Retention-based forgiveness schedule applies after program completion.</li></ul><p><strong>So what are the next steps?</strong></p><p>Our team is eager to learn about you! Send us your resume or LinkedIn profile below and we'll explore working together!</p>