Senior AI/ML Engineer

Senior AI/ML Engineer

About the Role

We're seeking a Senior AI/ML Engineer to lead the design, development, and deployment of ML/AI solutions, agents, and data automations across our AWS-based analytics platform. In this role, you'll architect end-to-end ML systems, set technical direction, mentor junior engineers and analysts, and drive best practices in MLOps and ML infrastructure. This is a high-impact role for an experienced engineer who can own complex, ambiguous problems and deliver production-grade machine learning and AI agent solutions.

Key Responsibilities

Architect, train, evaluate, and optimize machine learning models, owning the full model lifecycle from experimentation to production

Design and build AI agents and automated workflows using Amazon Quick and AWS orchestration tools

Define and implement efficient ML workflows, optimizing for performance, scalability, and cost

Architect and maintain serverless data pipelines using AWS Glue, Step Functions, Lambda, and EventBridge Scheduler

Lead the design of our analytics service engine for ingesting, transforming, and querying data across S3 storage (Excel/CSV files, Delta Tables, library files)

Establish MLOps practices, CI/CD pipelines, and infrastructure standards for the team

Integrate with external systems (e.g., SAP, Salesforce) and design robust data-sourcing strategies

Design and build REST APIs and model-serving infrastructure for production workloads

Mentor junior engineers, conduct code reviews, and set technical standards

Partner with cross-functional stakeholders to translate business needs into ML solutions

Required Technical Skills

Programming: Expert in Python with a track record of writing clean, well-tested, production-grade code

ML Frameworks: Strong, hands-on experience with PyTorch, TensorFlow, and scikit-learn

Model Development: Deep understanding of model training, evaluation, inference, and optimization for efficient ML at scale

AI Agents & Automation: Proven experience building AI agents and automated workflows; proficient with Amazon Quick

MCP & Tool Integration: Experience building and integrating Model Context Protocol (MCP) servers to connect LLMs and AI agents with external tools, data sources, and services

APIs & Serving: Strong experience designing REST APIs and deploying/serving ML models in production

Cloud & Infrastructure: Solid experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker)

MLOps & Tooling: Proficient with Git, CI/CD pipelines, and ML infrastructure best practices

Familiarity with Our Architecture

Our team's analytics platform is built on AWS. Deep familiarity with the following components is expected, and you'll help shape how we use and evolve them:

Orchestration & Compute: AWS Glue, AWS Step Functions, AWS Lambda, EventBridge Scheduler

Storage & Data: S3 (CSV/Excel, Delta Tables, library files), Glue Data Catalog, Glue Crawler

Query & Analytics: Amazon Athena

AI & Automation: Amazon Quick

Integration: External systems such as SAP and Salesforce

Notifications: Amazon SNS and Amazon SES for alerting and email

Infrastructure & Security: AWS IAM, AWS Secrets Manager, CloudWatch, AWS Systems Manager (for environment parameters)\

Source Control & CI/CD: Bitbucket for version control, pull request workflows, and pipeline-based deployments

Core Competencies

Problem-Solving: Independently solves complex, ambiguous ML and automation challenges and designs scalable solutions

Technical Leadership: Sets technical direction, drives architecture decisions, and mentors junior engineers

Collaboration: Leads cross-functional initiatives and owns the delivery of significant components end-to-end

Continuous Improvement: Champions MLOps, software engineering best practices, and ML infrastructure across the team

Code Quality: Sets and enforces high standards through clean, testable code, rigorous reviews, and robust version control