Director, AI/ML Engineering

Note: Fidelity will not provide immigration sponsorship for this position.

Position Description:

Leads artificial intelligence (AI) and machine learning (ML) initiatives by working closely with data scientists, engineers, and stakeholders to architect, design, develop, and operate enterprise solutions. Designs and implements application and service-based architectures to support AI and ML workloads. Manages and provisions cloud-based environments to enable scalable, secure, and resilient platforms. Implements advanced AI capabilities to support business use cases and intelligent automation. Builds and integrates workflows to support orchestration and lifecycle management of AI and ML solutions. Orchestrates and monitors AI and ML applications within cloud environments. Supports deployment, inference, tuning, and measurement of ML models. Evaluates and modernizes existing systems and workflows to improve operational efficiency and system capabilities. Collaborates with data scientists to develop analytics and ML platforms that enable prediction and optimization. Develops deployment pipelines and operational processes to support solution delivery. Creates monitoring and observability capabilities to ensure application performance and reliability.

Primary Responsibilities:

• Translates and incorporates business vision and strategy to AI or ML architectural strategy recommendations.

• Participates in high-level, cross-functional design teams.

• Identifies and consults with internal and external technical resources to produce cross-company

• strategic designs.

• Consults on development and delivery of major technology initiatives for the business unit.

• Consults on deployment of major project deliverables.

• Consults on the documentation of major technology applications.

• Oversees the technical implementation of cross-divisional or company architectural components.

• Initiates and drives project or strategy discussions with users or external groups to resolve issues.

• Establishes best practices and develops technical documentation to support standardization and knowledge sharing across engineering teams.

• Sets vision, goals, and direction of team/organization.

• Plans and leads organization-wide initiatives.

• Provides leadership, technical supervision, and expertise to multiple teams in broad technical areas on complex organization-wide projects.

• Provides technical leadership through mentoring, architectural guidance, and peer reviews.

• Advises senior management on technical strategy.

• Researches and recommends new technologies.

• Works across groups to identify opportunities for organization-wide technology initiatives.

• Regularly provides guidance, training, and coaching to other team members for performance and career development.

• Identifies and plans for future resource needs.

• Determines technical approaches at a strategic level for the business unit.

Education and Experience:

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and six (6) years of experience as a Director, AI/ML Engineering (or closely related occupation) architecting and developing intelligent digital business systems that integrate AI and ML with micro-services using Cloud-native technologies in a financial services environment.

Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and four (4) years of experience as a Director, AI/ML Engineering (or closely related occupation) architecting and developing intelligent digital business systems that integrate AI and ML with micro-services using Cloud-native technologies in a financial services environment.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) architecting, designing, and building feature engineering pipelines, deploying AI models, and optimizing model inference using PyTorch or Amazon Bedrock; developing ML infrastructure and MLOps in the Cloud using Amazon Web Services (AWS) -- SageMaker, Lambda, Glue, and Step functions; designing systems to automate synthetic data generation and train models using Python and Sagemaker; and working with predictive and optimization ML models in a development and production environment for deployment, inference, tuning, and required measurements.

• DE architecting, designing, and building highly scalable Cloud-based Big Data applications according to business user requirements in AWS, using S3, EMR, Lambda, and Athena; acting as a member of a team responsible for implementing data lake strategies using Snowflake as a platform for structured and semi-structured data; and building and formulating data lake design patterns for data ingestion, processing, and extraction for personalization teams using Snowflake, SQL, Python, data warehousing, and advanced data modeling techniques (Entity-Relationship and Dimensional mode).

• DE architecting, designing, and building Retrieval Augmented Generation (RAG) techniques with vector databases to enhance the capabilities of generative AI and LLMs; designing and creating end-to-end applications for document classification and intelligent data extraction using LLMs and fine-tuned ML models; designing and implementing evaluation frameworks with quality metrics, ground truth annotation workflows in Label Studio, and interactive data visualization dashboards in Tableau, including Sankey charts, to support model performance analysis and continuous improvement; and developing API frameworks to support real time and near real time ingestion of customer interaction data from multiple channels, with integration into managed streaming and delivery services (Kinesis Streams and Firehose).

• DE creating and maintaining modularized pipeline framework for establishing Continuous Integration/Continuous Delivery (CI/CD) pipelines for applications using Docker, Jenkins, Artifactory, SonarQube and GitHub; developing Unix shell scripts and creating CLI utilities to automate end-to-end processes, including testing, deployment and validation across development and production environments; performing platform migration by transitioning on-premises systems to AWS cloud infrastructure; and ensuring the full potential of cloud-based environments and modern data warehousing technologies (Star Schema or Snowflake Schema) using End-to-End (E2E) migration planning, execution, and optimization.

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Fidelity’s Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.

Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.