Job DescriptionWhat is this position about?
Lead end-to-end project delivery with clear governance and strong stakeholder communicationMentor junior engineers and contribute to proposals and new business initiativesDefine what AI systems should and should not attempt, and communicate risks and tradeoffs transparently to clientsDesign and build RAG systems, agentic frameworks, and LLM-powered solutions robust enough for productionApply advanced prompt engineering techniques, including instruction design, few-shot sets, structured outputs, and tool/agent promptsLead feasibility assessments to select the right approach among prompting, RAG, fine-tuning, or classical MLDesign evaluation frameworks, including LLM-as-a-judge methods, custom metrics (recall@k, precision@k), and go/no-go gatesRun structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence rather than intuitionIdentify and categorize model failure modes, including hallucinations, retrieval misses, and instruction-following errorsBuild scalable inference infrastructure and CI/CD pipelines for AI/ML modelsAutomate the full MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retrainingDesign APIs, microservices, and orchestration layers optimized for latency, cost, and reliabilityQualificationsExpert-level Python, strong Git practices, and experience with ML/LLM versioningSolid cloud experience across AWS, Azure, or GCP (Azure preferred), plus containerization and orchestrationHands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluationProven MLOps/LLMOps track record using tools such as MLflow, Weights & Biases, or similarPractical evaluation design skills, including metrics, dataset curation,



and structured experimentationExperience with event-driven architectures, APIs, and microservicesStrong communication skills, equally comfortable engaging engineering teams and senior stakeholdersPreferred: experience with the Databricks MLOps platform, LLM fine-tuning, building agentic GenAI systems, Infrastructure as Code, security and observability for AI services, a classical ML background, and open-source contributionsWhat about languages?
English: Advanced (required for effective communication with global teams)How much experience must I have?
6+ years of experience building and deploying AI solutions in production environments, with a strong track record across RAG, agentic systems, and MLOps/LLMOps.Additional InformationOur 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.

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