AI Engineering Lead

This is a fully remote position, open to applicants in Argentina.

📋 Description

• Oversee comprehensive project delivery, ensuring effective governance and robust communication with stakeholders.

• Guide junior engineers while actively participating in proposals and new business ventures.

• Establish what AI systems should and should not pursue, transparently communicating risks and trade-offs to clients.

• Create and implement production-ready RAG systems, agentic frameworks, and solutions powered by LLM.

• Utilize advanced prompt engineering methods, including instructional design, few-shot sets, structured outputs, and tool/agent prompts.

• Conduct feasibility studies involving prompting, RAG, fine-tuning, and traditional machine learning.

• Develop evaluation frameworks that employ LLM-as-a-judge methodologies, custom metrics, and go/no-go gates.

• Execute structured experiments focusing on prompts, retrievers, chunking strategies, and models.

• Detect and classify model failure modes, such as hallucinations, retrieval misses, and instruction-following mistakes.

• Construct scalable inference infrastructure and CI/CD pipelines for AI/ML models.

• Streamline the MLOps/LLMOps lifecycle with automation processes for tracking, versioning, deployment, monitoring, and retraining.

• Design APIs, microservices, and orchestration layers optimized for latency, cost, and reliability.

⛳️ Requirements

• Proficient in Python at an expert level.

• Strong knowledge of Git practices.

• Experience with ML/LLM versioning.

• Extensive cloud experience, particularly with AWS, Azure, or GCP; Azure is preferred.

• Familiarity with containerization and orchestration.

• Practical experience in RAG covering chunking, embeddings, retrieval, reranking, and evaluation.

• Demonstrated MLOps/LLMOps experience using tools like MLflow, Weights & Biases, or similar.

• Competence in evaluation design, including metrics, dataset curation, and structured experimentation.

• Experience with event-driven architectures, APIs, and microservices.

• Excellent communication skills when working with engineering teams and senior stakeholders.

• Advanced English proficiency required for effective communication with global teams.

• A minimum of 6 years of experience in building and deploying AI solutions within production environments.

• Preferred expertise in Databricks MLOps, LLM fine-tuning, agentic GenAI systems, Infrastructure as Code, security and observability for AI services, classical ML, and contributions to open-source projects.

🏝️ Benefits

• Certifications in AWS, Databricks, and Snowflake.

• Access to AI learning paths.

• Customized study plans, courses, and additional certifications relevant to the role.

• Access to Udemy Business.

• English language lessons.

• Travel opportunities for attending industry conferences and client meetings.

• Career development plans and mentorship programs.

• Special rewards for birthdays, work anniversaries, and other personal milestones.

• Equipment provided by the company.

• Flexible working arrangements.

• Additional benefits may vary based on your location in LATAM.