Lead Data Engineer
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Job Description
Lead Data Engineer responsible for developing AI-driven data pipelines and infrastructure. Collaborate with the CTO and build a team within a fully remote environment.
Responsibilities:
- Partner with the CTO and leadership to set the Intelligence strategy and roadmap; own the execution.
- Build, hire, and develop the Intelligence team — set the bar for craft, shape the operating cadence, and build the collaboration patterns with product, platform, and engineering.
- Stand up the canonical data substrate: schema discipline, tenancy isolation, data contracts, lineage, and governance that AI/ML workloads run cleanly against.
- Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, and MLOps practice.
- Lead the learning and reasoning capabilities of the platform: RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence.
- Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness — make AI workloads observable to engineering, product, and customer success.
- Drive the build-vs-buy posture for the AI/ML stack; set production readiness standards for AI workloads in close collaboration with the platform team.
- Partner with product on the AI use case portfolio; engage directly with customers when needed to ground Intelligence decisions in real workflow problems.
Requirements:
- 10+ years of professional experience in AI/ML, data engineering, or data science, with 4+ years in formal leadership roles (Senior Manager, Director, or Head of) at a B2B SaaS or AI/ML platform company.
- Demonstrated track record of building and leading AI/ML or data teams of 5–15 people, with a strong hiring track record in the AI/ML market within the last two to three years.
- Deep technical credibility across the modern AI/ML stack: data platforms (Postgres, pgvector, MongoDB or equivalent), ML platforms (training, serving, MLOps), and generative AI (LLMs, embeddings, RAG, fine-tuning, evals).
- Experience shipping production ML and AI workloads to enterprise customers with the trust patterns that come with it: evals, observability, drift detection, confidence scoring.
- Excellent communication across all audiences — engineers, product, executives, and customers; strong cross-functional partnership instincts with product, engineering, and customer-facing teams.
Benefits:
- Projects ranging from massive batch processing to real-time streaming and event-driven architectures.
- Exposure to the cutting edge of AI Engineering: integrating Vector Databases and preparing unstructured data (text, images).
- Opportunity to work with top-tier open-source orchestration and processing tools (Airflow, Spark, Kafka).
- A culture of continuous learning
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