Director, AI/ML Engineering

Fidelity Financial Services

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Quick summary

Work type: On-site
Location: Westlake, TX
Posted: 1 day ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $236k

$169k most similar roles pay here $308k

This listing doesn't post a salary. Most similar roles pay $193,225–$278,000.

Based on 240 similar postings.

Employer

About Fidelity Financial Services

Fidelity Investments is one of the largest financial services companies in the world, offering brokerage services, mutual funds, retirement planning, wealth management, and life insurance to individuals and institutions. Industry: Financial Services & Investment Management

Fidelity Financial Services currently has 47 open roles on FindRole.

Listed pay typically runs $123,500–$185,000 across 10 roles with salary data.

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At a glance

TL;DR · Director, AI/ML Engineering

Role Posting Log in to save

The Director, AI/ML Engineering leads initiatives to architect, design, and operate enterprise solutions by collaborating with data scientists and engineers. This role involves building scalable cloud-based environments, developing deployment pipelines, and creating monitoring capabilities for AI and ML workloads. The individual will manage the lifecycle of machine learning models, including inference tuning and measurement, while modernizing existing systems to improve operational efficiency. Key technical requirements include expertise in PyTorch, Amazon Bedrock, Python, SQL, and AWS services like SageMaker, Lambda, Glue, and Step Functions. Additionally, the role requires experience with Snowflake for data warehousing, RAG techniques with vector databases, and CI/CD pipelines using Docker, Jenkins, and GitHub. The work focuses on developing intelligent digital business systems within a financial services environment to solve problems related to prediction, optimization, and automated data extraction.

Skills

AI Machine Learning MLOps Python AWS SageMaker Lambda Glue Step Functions S3 EMR Athena Snowflake SQL PyTorch Amazon Bedrock LLMs RAG Vector Databases Tableau Kinesis Docker Jenkins Artifactory SonarQube GitHub CI/CD

What you'll do

Architect and design scalable cloud-based infrastructure to support AI and ML workloads.
Develop and manage MLOps pipelines for model deployment, inference, tuning, and performance measurement.
Build and integrate automated workflows for the orchestration and lifecycle management of machine learning solutions.
Design Retrieval Augmented Generation (RAG) techniques using vector databases to enhance generative AI capabilities.
Create CI/CD pipelines and automation scripts to streamline software delivery across development and production environments.
Develop big data applications and data lake strategies using Snowflake, AWS, and advanced modeling techniques.
Establish technical standards, best practices, and documentation to ensure consistency across engineering teams.
Provide strategic leadership and technical mentorship to guide cross-functional teams on complex AI initiatives.

What we're looking for

Bachelor's degree in Computer Science, Engineering, Information Technology, Information Systems, or a related field and six years of experience as a Director, AI/ML Engineering.
Master's degree in Computer Science, Engineering, Information Technology, Information Systems, or a related field and four years of experience as a Director, AI/ML Engineering.
Experience architecting and developing intelligent digital business systems integrating AI and ML with micro-services using Cloud-native technologies in financial services.
Expertise in building feature engineering pipelines, deploying models, and optimizing inference using PyTorch or Amazon Bedrock.
Expertise in developing ML infrastructure and MLOps in AWS using SageMaker, Lambda, Glue, and Step Functions.
Experience designing Big Data applications in AWS (S3, EMR, Lambda, Athena) and implementing data lake strategies using Snowflake.
Expertise in RAG techniques with vector databases, LLMs, and building evaluation frameworks with tools like Label Studio and Tableau.
Experience creating CI/CD pipelines using Docker, Jenkins, Artifactory, SonarQube, and GitHub to automate end-to-end processes.

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