AI Engineering Manager
RemoteMexico
Job Summary
Lead end-to-end project delivery with clear governance, stakeholder communication, and accountability for outcomes. Build and mentor a high-performing AI engineering team while establishing technical standards and fostering a culture of quality. Own proposals and new business initiatives by defining technical feasibility and communicating risks to clients. Guide the design and delivery of RAG systems, agentic frameworks, and LLM-powered solutions robust enough for production. Design evaluation frameworks including LLM-as-a-judge approaches and lead structured experiments grounded in evidence. Build scalable inference infrastructure and automate the full MLOps/LLMOps lifecycle for tracking, versioning, and deployment. Blend is a premier AI services provider committed to co-creating meaningful impact for clients through data science and technology.
Required Qualifications
- 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
- Experience with ML/LLM versioning and deployment
- Solid cloud experience across AWS, Azure, or GCP
- Containerisation knowledge
- 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
- Experience with APIs
- Experience with microservices
- A clear communicator equally comfortable with engineering teams and senior stakeholders
- Strong hiring and team-building instincts
- Proven mentoring experience
- English: Advanced
- 7+ years of hands-on AI/ML engineering experience in production environments
- 2+ years of direct team leadership or technical management responsibility
Desired Qualifications
- preference for Azure
- 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
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