Director AI ML Engineering
Fidelity Investments is a privately held, Boston-headquartered financial services company providing investing, trading, retirement, wealth management, brokerage, workplace benefits, custody and clearing, and digital-asset services.
Maintainer signals as of 9/25/2026
About Fidelity Investments
Fidelity serves individual investors, employers, wealth-management firms, institutions, innovators, and charitable donors through diversified financial-services businesses. Current offerings include brokerage and trading, mutual funds and exchange-traded products, retirement and workplace savings, financial planning and advice, wealth management, institutional custody and clearing, and crypto/digital-asset products.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead enterprise AI and machine learning initiatives, architect cloud-native platforms, deploy and monitor models, and build operational pipelines. You will guide technical strategy, lead cross-functional initiatives, mentor engineers, and establish engineering standards.
Requirements
- Bachelor's degree and six years of relevant experience, or master's degree and four years of relevant experience
- Experience architecting intelligent digital business systems integrating AI and ML with microservices and cloud-native technologies in financial services
- PyTorch
- Amazon Bedrock
- AWS SageMaker
- AWS Lambda
- AWS Glue
- AWS Step Functions
- Python
- AWS S3
- AWS EMR
- AWS Athena
- Snowflake
- SQL
- Data warehousing
- Data modeling
- Retrieval-augmented generation
- Vector database
- Large language model
- Label Studio
- Tableau
- Kinesis Streams
- Kinesis Firehose
- Docker
- Jenkins
- Artifactory
- SonarQube
- GitHub
- Unix shell scripting
Responsibilities
- Lead AI and machine learning initiatives
- Architect and develop enterprise AI and ML solutions
- Design application and service architectures for AI and ML workloads
- Manage cloud-based environments
- Deploy, tune, measure, and monitor machine learning models
- Build deployment pipelines and operational processes
- Lead organization-wide technology initiatives
- Provide technical leadership, mentoring, and architectural guidance
- Establish engineering best practices and technical documentation
Benefits
- Fully paid parental leave
- On-site health and wellness centers