Senior AI Engineer

Blend360 · Posted 2d ago

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Eligibility

Uruguay

Type

Full-time

Posted

2d ago

Tech

PythonMLflowAWSGCPAzureDatabricks

Company 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 Senior AI Engineer to contribute to our next level of growth and expansion.

What is this position about?

We are looking for a Senior AI Engineer to join our LATAM team in a remote, full-time role. This position is focused on designing, building, and deploying production-ready AI systems that create meaningful impact for our clients. The ideal candidate will work across the full stack of modern AI delivery, including RAG pipelines, agentic frameworks, LLM-powered solutions, evaluation design, and MLOps/LLMOps.

This role requires a senior technical voice who can lead end-to-end delivery, mentor other engineers, communicate effectively with stakeholders, and help clients make pragmatic decisions about what AI systems should and should not attempt.

Job Description

As part of this role, you will be responsible for:

Leading AI project delivery end to end, ensuring clear governance, strong stakeholder communication, and reliable execution.
Designing and building robust RAG systems, agentic frameworks, and LLM-powered solutions suitable for production environments.
Applying advanced prompt engineering techniques, including instruction design, few-shot prompting, structured outputs, and tool/agent prompts.
Leading feasibility assessments to determine the right technical approach, including prompting, RAG, fine-tuning, classical ML, or hybrid solutions.
Designing evaluation frameworks for AI systems, including LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go gates.
Running structured experiments across prompts, retrievers, chunking strategies, embeddings, reranking approaches, and models.
Identifying and categorizing model failures such as hallucinations, retrieval misses, instruction-following errors, and quality regressions.
Building scalable inference infrastructure and CI/CD pipelines for AI and ML models.
Automating the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, retraining, and continuous improvement.
Designing APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and scalability.
Mentoring junior engineers and contributing to proposals, solution design, and new business initiatives.

Qualifications

The ideal candidate should have:

6+ years of experience building and deploying AI, ML, or data-driven solutions in production environments.
Strong expertise in Python and solid Git practices.
Hands-on experience with LLM-powered solutions, RAG systems, and modern GenAI development patterns.
Practical experience with RAG components, including chunking, embeddings, retrieval, reranking, and evaluation.
Strong understanding of prompt engineering techniques, including structured outputs, few-shot prompting, instruction design, and tool/agent prompts.
Proven experience designing evaluation strategies for AI systems, including metrics, dataset curation, structured experimentation, and quality gates.
Experience with MLOps/LLMOps practices and tools such as MLflow, Weights & Biases, or similar platforms.
Solid cloud experience with AWS, Azure, or GCP. Azure experience is preferred.
Experience with containerization, orchestration, scalable inference, APIs, and microservices.
Understanding of event-driven architectures and production-grade engineering practices.
Ability to communicate clearly with engineering teams, senior stakeholders, and clients.
A pragmatic approach to AI delivery, balancing innovation, reliability, cost, latency, and business value.

Nice to have: