Our Client Currently looking for Lead Data Engineer & Modeler, AI

What You’ll Do

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.

What You’ll Bring

• 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.
• 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.

• Are you interested in this position?

   

  Apply by clicking on the “Apply Now” button below!

   

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