Mid-level AI Engineer, MLOps, AWS

Job not on LinkedIn

🕒 August 21

🇧🇷 Brazil – Remote

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Leega

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2010

💼 Consulting

📣 Marketing

🔌 API

Consulting • Marketing • API

Leega is a leading technology solutions provider in Latin America, specializing in data analytics and cloud solutions. As the first company in the region certified by Google Cloud for Data Analytics, Leega offers a range of services including application development, machine learning, and risk management analytics. The firm partners with major cloud services such as AWS and Microsoft Azure to help businesses enhance their data management and transition effectively to the cloud, ultimately driving digital transformation and innovation.

📋 Description

• Productize Machine Learning and Generative AI models, turning experiments into stable production services • Build and maintain MLOps pipelines for training, versioning, deployment, and reprocessing • Implement scalable deployments, balancing performance, availability, and cost • Develop and train Machine Learning models for business problems • Build Generative AI and LLM solutions, integrating them into products and internal processes • Monitor production models, tracking quality, degradation, and unexpected behavior • Apply security practices, access control, and protection of sensitive data • Evaluate results and propose improvements based on technical and business metrics • Collaborate with data, engineering, and business teams to define and prioritize use cases

🎯 Requirements

• Experience productizing AI and Machine Learning solutions, taking models from experiments to production • Technical autonomy to build and maintain end-to-end MLOps pipelines • Experience with scalable model deployment and continuous production monitoring • Hands-on experience with AWS AI and Machine Learning services: SageMaker and Bedrock • Proficiency in Python applied to AI and Machine Learning solutions • Knowledge of SQL and data manipulation for dataset preparation • Knowledge of security practices applied to AI solutions and handling sensitive data • Experience developing and training in-house Machine Learning models using scikit-learn, PyTorch, or TensorFlow • Experience integrating third-party LLMs via API • Experience with LangChain, LangGraph, LlamaIndex, or similar • Experience with RAG and vector databases • Experience with containers and orchestration, such as Docker and Kubernetes • AWS certifications focused on Machine Learning, AI, or solutions architecture • Previous consulting experience or servicing multiple clients • Autonomy, critical thinking, clear communication, rigor in evaluating results, technical curiosity, discipline, and collaboration • No explicit educational requirement stated in the listing

🏖️ Benefits

• Ongoing training and development • Company invests in its employees • Position also open to candidates with disabilities