Lead Data Engineer & Modeler, AI - Hybrid

BigCommerce•Austin, TX

38d•$116,000 - $174,000•Hybrid

About The Position

At Commerce, our mission is to empower businesses to innovate, grow, and thrive with our open, AI-driven commerce ecosystem. As the parent company of BigCommerce, Feedonomics, and Makeswift, we connect the tools and systems that power growth, enabling businesses to unlock the full potential of their data, deliver seamless and personalized experiences across every channel, and adapt swiftly to an ever-changing market. Simply said, we help businesses confidently solve complex commerce challenges so they can build smarter, adapt faster, and grow on their own terms. If you want to be part of a team of bold builders, sharp thinkers, and technical trailblazers, working together to shape the future of commerce, this is the place for you. BigCommerce is building the foundation for the next generation of AI-driven commerce. As a Lead AI Engineer, Platform & Infrastructure, you'll define and scale the systems that make this transformation possible. This role sits at the intersection of data engineering, MLOps, and applied AI enablement, responsible for building the secure, scalable, and high-performance infrastructure that supports AI/ML use cases across the company. You'll collaborate across product, engineering, and data teams to design the unified AI platform layer - powering internal intelligence, customer-facing AI features, and advanced analytics. From model lifecycle management to data pipelines and inference infrastructure, you'll drive the architecture and operational excellence that allows BigCommerce to experiment, deploy, and iterate AI at scale. If you're passionate about enabling intelligence through infrastructure, designing modern ML ecosystems, and operationalizing AI across a fast-scaling SaaS platform, this role will put you at the center of BigCommerce's AI evolution.

Requirements

7+ years in data or ML engineering, with experience designing production-grade AI infrastructure.
Strong technical foundation in MLOps, data pipelines, and distributed systems.
Hands-on experience with:
Cloud AI platforms (Vertex AI, SageMaker, Bedrock, or equivalent)
Orchestration frameworks (Airflow, Kubeflow, MLflow, or Metaflow)
Cloud data stacks (BigQuery, Snowflake, GCS/S3, Terraform)
Model serving tools (FastAPI, BentoML, Ray Serve, or Triton Inference Server)
Proficient in: Python, SQL, and Git-based CI/CD.

Nice To Haves

Experience integrating LLMs and vector databases (e.g., Pinecone, FAISS, Weaviate, Vertex Matching Engine).
Familiarity with Kubernetes, Docker, and Terraform for scalable deployment.
Strong communication skills, able to partner across disciplines and simplify complex technical systems.

Responsibilities

AI Platform Architecture
Partner with the Enterprise Architect and Principal Data Architect to design the company-wide AI/ML platform strategy across GCP and AWS.
Build scalable systems for model training, evaluation, deployment, and monitoring.
Define best practices for data ingestion, feature stores, vector databases, and model registries.
Integrate AI workflows into existing analytics and product pipelines.
Infrastructure & Reliability
Implement CI/CD for ML pipelines (MLOps) including model versioning, validation, and automated deployment.
Ensure platform reliability, observability, and performance at enterprise scale.
Manage GPU/TPU resources and optimize compute efficiency for training and inference workloads.
Contribute to cost-optimization and security best practices across the AI infrastructure.
Cross-Functional Collaboration
Partner with data scientists, applied ML engineers, and product teams to translate model requirements into scalable architecture.
Work closely with the data engineering team to ensure AI pipelines align with governance and data quality standards.
Collaborate with software engineers to integrate AI services and APIs into production systems.
Governance & Responsible AI
Champion data and model governance, including lineage, reproducibility, and compliance (GDPR, SOC, ISO).
Establish monitoring frameworks for model drift, bias detection, and ethical AI use.
Build secure and transparent systems that support trust in AI-driven decisions.