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
We are seeking an
AI Engineering Manager
to contribute to our next level of growth and expansion.
Job Description
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
Qualifications
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
Additional Information
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
⚖️
Adaptable 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!

📌 Ai Engineering Manager (Buenos Aires)
🏢 Blend360
📍 Buenos Aires