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!