AI/ML Architect with Databricks , AWS

AI/ML Architect with Databricks, AWS

Location: Los Angeles CA or NYC (Hybrid)

Exp: 12+ Years

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.

Skills & Qualifications

Required

• Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, or related field.
• 10+ years of experience in data engineering, ML engineering, or AI/ML architecture roles.
• Deep expertise in Databricks on AWS, including:
• PySpark / Spark SQL
• Databricks Notebooks
• Delta Lake
• Unity Catalog
• MLflow
• Databricks Jobs & Workflows
• Strong programming ability in Python (pandas, numpy, scikit-learn).
• Demonstrated experience with large-scale, multi-terabyte data processing.
• Strong understanding of ML algorithms, distributed systems, and data optimization.

Preferred

• Experience with MLOps and production deployment pipelines.
• Strong grasp of AWS-native data and compute services.
• Understanding of CI/CD using GitHub Actions, GitLab CI, or similar.
• Familiarity with deep learning frameworks (TensorFlow, PyTorch).

Key Competencies

• Strong analytical and problem-solving skills.
• Ability to work in fast-paced, highly collaborative environments.
• Excellent communication and presentation abilities.
• Self-driven with exceptional attention to architectural detail.