AI Engineering Manager

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 (recall@k, precision@k), 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?
• 7+ years of hands-on AI/ML engineering experience in production environments, with 2+ years of direct team leadership or technical management responsibility.

Nice to Have

• Databricks MLOps platform
• LLM fine-tuning experience
• Building agentic GenAI systems
• Infrastructure as Code
• Security and observability for AI services
• Classical ML background
• Open-source contributions

Our Perks and Benefits

🏥 Health and Well-being

• At-home medical assistance via EMI (or similar provider) through Asobursatil, available for all employees from AllStar to Analyst level.
• Private healthcare plans for Lead-level roles and above.

🎉 Celebrations and Recognitions

• Christmas kit delivered to all employees.
• 1 day off for academic graduation.
• Family Day: 1 day off every semester (must be taken within the same semester).

💰 Financial Health and Savings (Work Together, Get Together Program)

• Savings incentive program via Asobursatil:
• Year 1: Blend contributes 50% of your monthly savings.
• Year 2: Blend contributes 100% of your monthly savings.
• Year 3+: Blend contributes 150% of your monthly savings.
• Savings can be withdrawn in July and December.

📚 Educational Loans and Subsidies

• Forgivable education loans subject to committee approval and budget availability.
• 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.
• Retention-based forgiveness schedule applies after program completion.

• So what are the next steps?
• Our team is eager to learn about you! Send us your resume or LinkedIn profile below and we'll explore working together!