Job ID: JOB_ID_5734
Role Overview
We are seeking an experienced AI/ML Architect with deep hands-on expertise in Databricks on AWS to lead the design and implementation of scalable, high-performance data and machine learning platforms. The ideal candidate combines architectural thinking with strong engineering execution, demonstrating the ability to build modern lakehouse systems, optimize large-scale pipelines, and drive analytical and ML capabilities across the organization.
This role requires working with large, multi-terabyte datasets, advanced analytics, and end-to-end ML lifecycle management using Databricks, Python, PySpark, and AWS-native services.
Must Demonstrate (Critical Competencies)
- Designing Databricks-based lakehouse architectures on AWS (Delta Lake + S3 + Unity Catalog).
- Clear separation of compute vs. serving layers in distributed architectures.
- Low-latency API strategy where Spark is insufficient (e.g., leveraging optimized services or caching).
- Caching strategies to accelerate reads and reduce compute cost.
- Data partitioning, file size tuning, and optimization strategies for large-scale pipelines.
- Experience handling multi-terabyte structured time-series workloads.
- Ability to distill architectural significance from ambiguous business requirements.
- Strong curiosity, questioning, and requirement-probing mindset.
- Player-coach approach: hands-on technical depth + ability to guide design.
Key Responsibilities
AI/ML & Advanced Analytics
- Develop, train, and optimize ML models using Python, PySpark, MLflow, and Databricks Machine Learning.
- Conduct exploratory data analysis (EDA) to identify patterns, trends, and insights in large datasets.
- Deploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines.
- Build analytics solutions such as forecasting, anomaly detection, segmentation, or recommendation systems.
- Design ML architectures aligned with Databricks Lakehouse on AWS.
Data Engineering & Lakehouse Architecture
- Architect and build scalable ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.
- Implement Delta Lake best practices, including OPTIMIZE, ZORDER, partitioning, and schema evolution.
- Design lakehouse layers (Bronze/Silver/Gold) with strong separation of compute and serving layers.
- Optimize cluster performance and jobs using Spark tuning, caching, and shuffle minimization.
- Work with multi-terabyte, time-series, high-velocity data in a distributed environment.
- Ensure robust data availability for downstream ML and analytics workloads.
AWS Cloud Integration
- Architect end-to-end data and ML solutions using AWS services, including:
- S3 for storage
- IAM for identity & access
- Glue Catalog for metadata management
- Networking for secure, high-throughput data movement
- Integrate Databricks with AWS-native compute, API layers, and low-latency endpoints.
Business Collaboration & Leadership
- Translate business problems into scalable analytical or ML architectures.
- Communicate complex statistical and architectural concepts to non-technical stakeholders.
- Collaborate with product, engineering, and business leaders to drive data-informed initiatives.
- Provide design leadership while remaining hands-on in execution.
Special Requirements
Visa: Open to candidates only with valid work authorization in USA. Interview mode: Onsite.
Compensation & Location
Salary: $140,000 – $200,000 per year (Estimated)
Location: Los Angeles, CA
Recruiter / Company – Contact Information
Email: irami@livemindz.com
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