Director, Software Engineering

About the role

Fidelity will not provide immigration sponsorship for this position.

Position Description

Oversees the building of data pipelines and the creation and evaluation of Machine Learning (ML) models using distributed data processing frameworks (Apache Spark). Leads the design and oversight of cross- organizational data engineering and ML platform initiatives, defining reference architectures, engineering standards, and governance frameworks.

Oversees end-to-end technical execution for large-scale, multi-team programs, including capacity planning, platform modernization, resiliency engineering, and cloud cost optimization. Defines technical success metrics and leads deep-dive reviews of architecture, performance, and system reliability for complex data analytics and ML infrastructure.

Identifies potential risks in model performance and ensures proactive measures are implemented to prevent degradation. Guides planning and execution of tools for detecting data drift, monitoring prediction uncertainty, and automating model explanations.

Primary Responsibilities

• Draws on in-depth knowledge of the business or function to provide business unit-wide solutions by developing complex, multi-faceted software applications.
• Researches and recommends new technologies in support of the strategic direction of the business unit and participates in the research and recommendation of appropriate models, methods, tools, and technologies to achieve business-unit-wide solutions.
• Translates and incorporates business vision and strategy to architectural strategy recommendations at the functional level.
• Participates in high-level, cross-functional architecture 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 application.
• 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.
• 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.
• 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, Software Engineering (or closely related occupation) designing, developing, and deploying end-to-end ML pipelines and scalable cloud infrastructure.

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, Software Engineering (or closely related occupation) designing, developing, and deploying end-to-end ML pipelines and scalable cloud infrastructure.

Skills and Knowledge

Candidate must also possess

• Demonstrated Expertise designing, developing, and deploying data features that enable ML models; developing and troubleshooting PyTorch on prem and cloud environments; developing dynamic feature generated platforms using Python and PostgreSQL; building and deploying ML models in Cloud (AWS Sagemaker), using Jenkins; orchestrating ML models within a Cloud environment to run periodically with required data features; and transforming model output to fit business use cases and making it available for consumers in Cloud-based data warehouse solutions -- Snowflake.
• Demonstrated Expertise architecting and deploying scalable cloud solutions using Amazon EC2, RDS, and SageMaker, with secure credential management via AWS Secrets Manager; building serverless applications with AWS Lambda and Lambda layers, orchestrating workflows using Step Functions, and implementing messaging services with SNS and SQS; performing infrastructure automation using CloudFormation and Terraform, and containerization with Docker; and performing data modeling and troubleshooting of big data applications, predictive analytics, and KPI dashboard development, using Python, and advanced SQL and Non-SQL queries with structured and unstructured databases.
• Demonstrated Expertise developing and deploying ML solutions using Amazon SageMaker; building robust APIs with FastAPI, and implementing MCP server and client architectures; designing and integrating A2A agentic frameworks to enable autonomous agent collaboration; and enhancing data-driven decision-making and application performance, using advanced information retrieval techniques and Retrieval-Augmented Generation (RAG).
• Demonstrated Expertise implementing big data computing strategies (Continuous Integration/Continuous Deployment and Delivery processes) on enterprise applications using Jenkins; and deploying big data applications according to standard security (IAM and fine grain access controls -- role/policy based access and encryption schemes) and architectural requirements (domain, data modeling, optimized file formats, and Big Data architectures -- Lambda Architecture, Data Lakes, and Data Virtualization) to Cloud, using Jenkins pipelines and Docker.
• #PE1M2
• #LI-DNI
• Fidelity’s Onsite Working Model

Additional information

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.