Lead AI Engineer

Blend; Buenos Aires; Hybrid; ARS 136,430,000 - 227,383,000; Full time

Benefits offered by this job

Certifications in AWS

Databricks

Snowflake

Udemy Business

English lessons

Career development plans

Company-provided equipment

Flexible working options

Travel opportunities

Job summary

Blend is seeking a Lead AI Engineer to drive growth and expansion, leading end-to-end AI projects with strong governance and client communication. You will mentor a high-performing AI engineering team, shaping technical standards and delivering production-ready RAG/LLM-based solutions.

Ideal candidates have 5+ years building AI in production, expert Python and ML/LLM deployment skills, and solid cloud experience across AWS/Azure/GCP.

Qualifications

• 5+ years building and deploying AI in production.
• Expert Python, Git, and ML/LLM deployment.
• Cloud experience across AWS, Azure, or GCP—prefer Azure.
• Hands-on RAG, embeddings, retrieval, reranking experience.
• Proven MLOps/LLMOps with MLflow or Weights & Biases.
• Strong evaluation design, metrics, and experiments.
• Experience with event-driven architectures, APIs, and microservices.
• Clear communicator with engineering teams and stakeholders.
• Strong hiring and mentoring experience.

Responsibilities

• Lead end-to-end project delivery with governance and outcomes.
• Mentor a high-performing AI engineering team.
• Define AI systems scope and manage client expectations.
• Perform architectural reviews to maintain standards.
• Design and deploy robust RAG and LLM-powered solutions.
• Lead experiments on prompts, retrievers, and models.
• Build scalable inference infrastructure and CI/CD.
• Establish quality and production reliability practices.

Skills

Python

Git

ML/LLM deployment

Cloud: AWS Azure GCP

Containerisation & orchestration

MLOps/LLMOps

API & microservices

Communication

Team leadership

Tools

MLflow

Weights & Biases

Databricks

Job description

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 a Lead AI Engineer 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

• 5+ years building and deploying AI solutions in production environments
• 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?

5+ 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

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.