Senior Data Architect, LLM/AI Platforms (Remote)

Jobgether SRL

United States

On-site

USD 180,000 - 240,000

Full time

2 days ago

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Benefits offered by this job

Flexible remote work opportunities

Health and wellness programs

Equity opportunities

Parental leave

Job summary

Exabyte seeks a Principal Data Engineer to architect and scale data platforms powering next‑gen LLM/AI platforms. You will lead data pipelines, modeling, and MLOps while mentoring engineers in a highly autonomous setting.

The role blends hands‑on engineering with platform architecture, focusing on performance, resilience, and cost efficiency at massive scale. Collaboration with data scientists and product managers is essential.

Qualifications

Master’s or PhD in Computer Science, Data Engineering, or related STEM field or equivalent practical experience.
10+ years in Data or Platform Engineering, incl. AI/ML/data science platforms at massive scale.
3+ years in Principal/Staff-level engineering with leadership and mentorship.
Hands-on LLM engineering: fine-tuning, prompt engineering, deployment, RAG, agentic workflows.
Designing large-scale distributed systems with sharding, partitioning, concurrency, fault tolerance.
Proficient in Python or JVM-based tech, writing clean, production-grade code.
Experience with Spark, Dask, Flink for distributed data processing.
Strong knowledge of AWS/GCP/OCI data services.
Docker and Kubernetes expertise.
Kafka or Pulsar for messaging/streaming.
Snowflake, BigQuery, Airflow, Kubeflow data orchestration.
MLOps tools: MLflow, SageMaker, Vertex AI.
Familiar with LangChain or LlamaIndex for agentic AI.
Solid engineering practices: reviews, testing, secure development.
Experience applying AI to automate workflows and drive business value.
Excellent communication and cross-team collaboration.
Direct experience deploying LLMs in production is a plus.
Background in cybersecurity/regulatory industries is a plus.
Open-source contributions a plus.

Responsibilities

Architect, implement, and optimize data platforms and pipelines for LLMs, RAG, and agentic systems at exabyte scale.
Drive adoption of agentic workflows to enable autonomous, data-driven capabilities.
Design scalable, fault-tolerant, secure, cost-efficient data solutions for rapid iteration.
Develop production-ready, well-tested code with emphasis on performance and reliability.
Lead data modeling, semantic cataloging, and architecture for AI/ML workloads.
Establish MLOps and DataOps practices for observability and recovery.
Own end-to-end lifecycle of critical data services from development to deployment.
Collaborate with researchers, product managers, and engineers to productionize research prototypes.
Lead workshops, reviews, and mentoring to raise technical standards across AI platforms.
Champion DevSecOps and secure development across distributed data environments.
Identify opportunities to improve platform performance and developer productivity.