Principal, AI/ML Engineer

Fidelity Investments • Boston, MA

18h • $107,000 - $216,000 • Hybrid

About The Position

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.

Requirements

• 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
• 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

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

Benefits

• comprehensive health care coverage and emotional well-being support
• market-leading retirement
• generous paid time off and parental leave
• charitable giving employee match program
• educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level