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 www.blend360.com
We are seeking an AI Engineering Manager to contribute to our next level of growth and expansion.
Job DescriptionLeadership 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:
📚 Learning Opportunities:
- Certifications in AWS (we are AWS Partners), Databricks, and Snowflake
- Access to AI learning paths to stay up to date with the latest technologies
- Study plans, courses, and additional certifications tailored to your role
- Access to Udemy Business, offering thousands of courses to boost your technical and soft skills
- English lessons to support your professional communication
👨🏽💻 Travel opportunities to attend industry conferences and meet clients
👩🏫 Mentoring and Development:
- Career development plans and mentorship programs to help shape your path
🎁 Celebrations & Support:
- Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones
- Company-provided equipment
⚖️ Flexible working options to help you strike the right balance
Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.
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!
Skills Required
- 7+ years building and deploying AI/ML/LLM solutions in production environments
- 2+ years of direct team leadership or technical management experience
- Expert Python proficiency and strong Git practices
- Experience with ML/LLM versioning and deployment (MLOps/LLMOps)
- Cloud experience with AWS, Azure, or GCP (preference for Azure)
- Containerization and orchestration knowledge (Docker, Kubernetes)
- Hands-on RAG experience (chunking, embeddings, retrieval, reranking, evaluation)
- Proven MLOps/LLMOps track record using tools like MLflow or Weights and Biases
- Practical evaluation design skills: metrics, dataset curation, structured experimentation
- Experience with event-driven architectures, APIs, and microservices
- Advanced English communication
- Strong hiring, mentoring, and team-building experience
- Databricks MLOps platform experience
- LLM fine-tuning experience
- Experience building agentic GenAI systems
- Infrastructure as Code experience
- Security and observability for AI services
- Classical ML background
- Open-source contributions
Blend360 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Blend360 and has not been reviewed or approved by Blend360.
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Fair & Transparent Compensation — Pay is considered fair-to-good by many, and public salary postings for common data roles indicate competitive packages in numerous markets. Feedback suggests overall company sentiment aligns with acceptable compensation relative to peers in consulting and analytics.
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Flexible Benefits — Flexible and remote/hybrid work arrangements are consistently highlighted in official materials and role descriptions. Feedback suggests flexibility is a meaningful part of the total rewards experience.
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Retirement Support — A 401(k) with company match is part of the core package. Feedback suggests retirement offerings are standard and contribute to a complete benefits set.
Blend360 Insights
What We Do
Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics. Blend360 has two divisions: Data Science Solutions: We work at the intersection of data, technology and analytics. Talent Solutions: We live and breathe the digital and talent marketplace.
