AWS Consultant – Machine Learning (ProServe Shared Delivery Team)

Opportunity Overview

Role Scope & Technical Architecture

As an AWS Professional Services (ProServe) Delivery Consultant, you serve as a trusted technical advisor, architecting end-to-end ML, MLOps, and Generative AI systems for enterprise clients:

[Customer Requirements & Architecture] ➔ [Data & Feature Pipelines] ➔ [Model Training & GenAI Integration] ➔ [MLOps Deployment & Monitoring]

Core Responsibilities

• End-to-End AI/ML & GenAI Implementation: Work directly with enterprise stakeholders to gather requirements, design cloud architectures, and deploy models (e.g., using Amazon SageMaker, Amazon Bedrock, and LangChain).

• High-Performance ML Pipelines: Design scalable, secure pipelines for data preprocessing, feature store management, distributed GPU training, hyperparameter tuning, and model hosting.

• MLOps & Infrastructure as Code: Establish MLOps frameworks using Terraform, AWS CDK, CloudFormation, Step Functions, Airflow, and containerization (Docker, ECS, EKS).

• Client Advisory & Knowledge Transfer: Guide enterprise teams through cloud migration strategies, AI governance, security compliance (HIPAA, GDPR), and mentor client engineering teams.

Candidate Eligibility & Technical Skills Matrix

Basic Qualifications

• Experience in Cloud Architecture: 5+ years of experience designing and implementing distributed computing solutions.

• Engineering Background: 5+ years in ML engineering, software development, or data engineering with a focus on high-throughput data/ML pipelines.

• Cloud ML Deployment: 3+ years hands-on experience building, hosting, and deploying predictive modeling or NLP models using cloud services (e.g., Amazon SageMaker).

• Language & Framework Proficiency: 3+ years in Python, SQL, and an additional language (Java, Scala, TypeScript); proficient with PyTorch or TensorFlow.

• Bilingualism: French and English fluency if located in Quebec.

Preferred Technical Stack

Selection Workflow & Evaluation Stages

[Application & Resume Screening] ➔ [Recruiter Screen] ➔ [Technical Phone Screen] ➔ [ProServe Interview Loop]

1. Application Review: Recruiter evaluation of cloud engineering history, MLOps stack, client-facing consulting experience, and AWS ecosystem exposure.

2. Technical Phone Assessment: Live coding (Python/SQL), system architecture design, and foundational questions on distributed training and ML operations.

3. ProServe Virtual Interview Loop (4–5 Rounds):

   • Technical System Design: Designing an enterprise-scale ML/GenAI platform on AWS.

   • Live Coding & MLOps: Live scripting session covering pipelines, Docker, or data manipulation.

   • Consulting & Customer Scenario: Assessing client management, architectural decision-making, and trade-off evaluation.

   • Amazon Leadership Principles: Scenario-based questions (Customer Obsession, Earn Trust, Dive Deep, Deliver Results).