Lead Data Engineer

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

📋 Description

• Collaborate with the CTO and leadership to define the Intelligence strategy and roadmap; take charge of its execution.

• Build, recruit, and nurture the Intelligence team — establish high standards for craftsmanship, shape operational rhythms, and create collaborative patterns with product, platform, and engineering teams.

• Implement the canonical data substrate: schema discipline, tenancy isolation, data contracts, lineage, and governance to ensure that AI/ML workloads operate smoothly.

• Establish the ML and AI platform: manage model lifecycle, feature store, vector store, training and serving infrastructure, and the MLOps practice.

• Lead the platform's learning and reasoning capabilities: develop RAG architectures, agentic data systems, knowledge graphs, and the methodologies that enable Stratus's data to evolve into platform intelligence.

• Create and drive evaluation frameworks to measure model quality, agent reliability, drift, and platform efficacy — ensure AI workloads are observable to engineering, product, and customer success teams.

• Influence the build-vs-buy strategy for the AI/ML stack; establish production readiness standards for AI workloads in close partnership with the platform team.

• Collaborate with product on the AI use case portfolio; engage directly with customers when necessary to ground Intelligence decisions in real-world workflow challenges.

⛳️ Requirements

• Over 10 years of professional experience in AI/ML, data engineering, or data science, including at least 4 years in formal leadership positions (Senior Manager, Director, or Head of) at a B2B SaaS or AI/ML platform organization.

• Proven history of building and leading AI/ML or data teams consisting of 5–15 members, with a strong hiring record in the AI/ML sector within the past two to three years.

• Significant technical credibility across the contemporary AI/ML stack: data platforms (Postgres, pgvector, MongoDB or equivalent), ML platforms (training, serving, MLOps), and generative AI (LLMs, embeddings, RAG, fine-tuning, evaluations).

• Experience delivering production ML and AI workloads to enterprise clients, including the necessary trust patterns: evaluations, observability, drift detection, and confidence scoring.

• Exceptional communication skills with all audiences — engineers, product teams, executives, and clients; strong instincts for cross-functional collaboration with product, engineering, and customer-facing teams.

🏝️ Benefits

• Projects that span massive batch processing to real-time streaming and event-driven architectures.

• Access to the forefront of AI Engineering: integrating Vector Databases and preparing unstructured data (text, images).

• Opportunity to engage with top-tier open-source orchestration and processing tools (Airflow, Spark, Kafka).

• A culture that promotes continuous learning.