Job Description:
The Role
We're seeking an exceptional Principal AI/ML Engineer to join our high-impact squad focused on transforming and growing our Financial Investment (FI) sales and platform business. In this role, you'll be at the intersection of cutting-edge AI technology and business strategy, driving innovation through opportunity lead generation, intelligent product recommendations, coverage strategy optimization, and engagement experimentation.
As a T-shaped AI professional, you'll bring deep technical expertise while demonstrating strong business acumen and cross-functional collaboration skills. Critically, we need someone who thinks in systems—understanding how each change ripples through the entire ecosystem—and who approaches every project with a production-ready mindset from day one, not just building shiny demos.
Key Responsibilities
AI/ML Innovation & Implementation
- Design and deploy state-of-the-art AI and ML solutions to accelerate FI business growth with production reliability and scalability as primary considerations
- Develop and optimize Large Language Model (LLM) applications for business use cases that integrate seamlessly into existing systems
- Implement context engineering strategies and prompt optimization techniques
- Build and maintain Retrieval-Augmented Generation (RAG) systems for semantic search and knowledge retrieval
- Research and prototype emerging AI techniques, always evaluating for production viability and system-wide impact
Systems Thinking & Architecture
- Analyze and anticipate how AI implementations affect upstream and downstream systems, data flows, and user experiences
- Design solutions considering scalability, maintainability, observability, and failure modes from the outset
- Evaluate technical decisions through the lens of system-wide performance, cost, and operational complexity
- Collaborate with platform, infrastructure, and application teams to ensure seamless integration
- Document system dependencies, data lineage, and architectural decisions for long-term maintainability
Business Partnership & Strategy
- Collaborate closely with business stakeholders to deeply understand challenges and translate them into technical solutions
- Define and monitor AI performance metrics aligned with business KPIs
- Balance innovation with pragmatism—prioritizing solutions that deliver measurable business value
Data Engineering & Architecture
- Design and implement robust ETL pipelines for both structured and unstructured data
- Build scalable data infrastructure supporting real-time and batch processing needs
- Perform exploratory data analysis to uncover insights and improvement opportunities
- Ensure data quality, governance, and security best practices
Deployment & Operations
- Deploy and manage AI/ML models in cloud environments (AWS) with production SLAs in mind
- Establish monitoring systems for model performance, drift detection, and system health
- Optimize model serving infrastructure for latency, throughput, and cost
- Implement MLOps best practices for continuous integration and deployment
- Build with observability, debugging, and incident response capabilities from the start
The Expertise and Skills You Bring
Education & Experience
- BS/MS in Engineering, Computer Science, Data Science, or related field
- 5-8+ years of software development experience with proven AI/ML project delivery in production environments
- Demonstrated ability to manage multiple concurrent projects in fast-paced environments
T-Shaped Expertise Profile
Deep Technical Skills (the vertical bar):
- LLM & Modern AI: Hands-on experience with Large Language Models, prompt engineering, context optimization, and fine-tuning techniques
- AI/ML Engineering: Expert knowledge of statistical models, predictive modeling, time series analysis (regression, classification, clustering, dimension reduction)
- Programming: Advanced proficiency in Python with object-oriented and functional programming paradigms
Broad Cross-Functional Skills (the horizontal bar):
- Business Acumen: Ability to understand financial services domain and translate business needs into technical solutions
- Data Engineering: Production experience with ETL pipeline tools (Airflow, dbt) and big data technologies (Snowflake)
- Deployment & MLOps: Experience deploying and managing applications in cloud environments (AWS preferred)
- Collaboration: Strong communication skills to work effectively with technical and non-technical stakeholders
Technical Stack Experience
- Python Ecosystem: NumPy, Pandas, Scikit-learn, Flask, Pip, Anaconda
- ML/AI Frameworks: Hugging Face, LangChain (or similar)
- Big Data Tools: Spark, Snowflake
- Cloud Platforms: AWS (SageMaker, Lambda, EC2, S3, etc.)
- Data Pipeline Tools: Airflow, dbt, or equivalent orchestration frameworks
- RAG & Vector Databases: Experience with semantic search, embeddings, and vector stores
Core Competencies
Production-First Mindset:
- Builds production-ready solutions from day one—not prototypes that need to be rebuilt
- Considers error handling, edge cases, monitoring, and operational concerns upfront
- Writes clean, maintainable, well-tested code that others can understand and extend
- Prioritizes reliability, performance, and user experience over technical novelty
Systems Thinking:
- Understands how changes propagate through complex systems and anticipates second-order effects
- Considers data dependencies, API contracts, backward compatibility, and migration paths
- Evaluates trade-offs holistically balancing technical debt, velocity, and long-term sustainability
- Thinks about failure scenarios, rollback strategies, and graceful degradation
The Team
Our team supports a variety of complex applications serving a diverse set of businesses and customers directly. The database environments are extremely critical to our success and vitality which in turn requires a unique technical aptitude.
The base salary range for this position is $107,000-216,000 USD per year.Placement in the range will vary based on job responsibilities and scope, geographic location, candidate’s relevant experience, and other factors.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.
We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.
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.
Most roles at Fidelity are Hybrid, requiring associates to work onsite every other week (all business days, M-F) in a Fidelity office. This does not apply to Remote or fully Onsite roles. Please consult with your recruiter for the specific expectations for this position.