Fidelity will not provide immigration sponsorship for this position.
Job Description:The Role
We are looking for a Senior Engineer who operates at the intersection of data and AI/ML/LLM Engineering. You will work on the end-to-end lifecycle from building robust data pipelines that feed AI systems, to deploying and operating AI/LLM and retrieval systems at scale. This is a hands-on engineering role, not data science.
- Real-time and batch data pipeline engineering on AWS and Snowflake
- Production-grade Python, testing, and CI/CD discipline
- AWS and Snowflake-based data and deployment patterns
- Productionizing AI/ML/LLM systems (serving, agents, retrieval pipelines)
- Observability, monitoring, and operational reliability of AI-powered systems
The Skills and Expertise You Bring
- Primarily hands-on, with the ability to raise technical standards through direct execution and engineering excellence.
- Design and build real-time and batch data pipelines: ingestion, transformation, streaming, and delivery into analytics and AI/ML systems
- Build resilient data connectors, chunking/embedding workflows, and parallel processing frameworks with data integrity guarantees
- Optimize ETL/ELT workflows in AWS and Snowflake including indexing, partitioning and efficient resource utilization
- Deploy, scale, and operate AI/ML/LLM services including model serving, agent orchestration, and retrieval pipelines
- Implement monitoring and observability for production AI systems for retrieval quality metrics, latency tracking, drift detection, and alerting
- Translate prototypes into production-grade systems that are pragmatic, hardware-efficient, and simple to operate
- Evaluate open-source AI/ML frameworks critically; adopt what's production-ready, replace bloat with lean, maintainable implementations
- Develop production-grade Python code with strong testing, clean interfaces, and maintainability practices
- Work within Git-based workflows and CI/CD pipelines to support reliable promotion across dev, UAT, and production environments
- Contribute to operational readiness through runbooks, incident response, and production support
- Strong Python skills for production data and AI/ML systems, including rigorous testing and maintainable code
- Strong SQL skills and experience with Snowflake for analytics and data pipeline workloads
- Experience with AWS services (EC2, S3, Lambda, Step Functions, SageMaker, IAM, RDS)
- Experience building and operating data pipelines using Spark, Airflow, or streaming frameworks (Kafka, Kinesis)
- Experience deploying and monitoring AI/ML/LLM services in containerized environments (Docker, Kubernetes)
- Experience with CI/CD and Infrastructure as Code (Terraform, CloudFormation, or similar)
- Comfort debugging complex distributed systems with evidence-driven approaches
- Experience with vector databases and retrieval-augmented generation (RAG) patterns
- Familiarity with LLM frameworks (LangChain, LlamaIndex, or similar) and agent orchestration
- Experience with observability tooling such as Datadog or CloudWatch
- Familiarity with event-driven architectures and real-time streaming systems
- Experience designing developer-facing APIs, SDKs, or CLI tools
- Understanding of agentic AI patterns and multi-step reasoning workflows
The Team
Our advanced analytics/insights team owns the data engineering, advanced analytics/insights, and production AI/ML/LLM Engineering for the HR technologies. Our work spans real-time and batch data infrastructure on AWS and Snowflake, analytics platforms, and the engineering backbone that makes AI/ML/LLM systems production-real. We are engineers who build with craft, ship with confidence, and take ownership of what runs.
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.
Skills Required
- Hands-on experience designing and building real-time and batch data pipelines, including ingestion, transformation, streaming, and delivery
- Strong production-grade Python skills, including testing, clean interfaces, and maintainable code
- Strong SQL skills and experience with Snowflake for analytics and data pipeline workloads
- Experience with AWS services including EC2, S3, Lambda, Step Functions, SageMaker, IAM, and RDS
- Experience building and operating data pipelines using Spark, Airflow, Kafka, Kinesis, or similar streaming frameworks
- Experience deploying and monitoring AI/ML/LLM services in containerized environments using Docker and Kubernetes
- Experience with CI/CD and infrastructure as code, such as Terraform or CloudFormation
- Experience with vector databases and retrieval-augmented generation patterns
- Experience with observability tooling such as Datadog or CloudWatch
- Experience designing developer-facing APIs, SDKs, or CLI tools
- Experience deploying, scaling, and operating AI/ML/LLM services, including model serving, agent orchestration, and retrieval pipelines
- Experience with monitoring and observability for production AI systems, including retrieval quality, latency, drift detection, and alerting
- Experience debugging complex distributed systems using evidence-driven approaches
- Familiarity with LLM frameworks such as LangChain or LlamaIndex and agent orchestration
- Familiarity with event-driven architectures and real-time streaming systems
- Understanding of agentic AI patterns and multi-step reasoning workflows
Fidelity Investments Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Fidelity Investments and has not been reviewed or approved by Fidelity Investments.
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Strong & Reliable Incentives — Bonuses, commissions, and profit-sharing are presented as generous and meaningful components of total compensation, with certain roles achieving high total earnings through multiple pay streams. Variable pay is consistently framed as a positive contributor beyond base salary.
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Retirement Support — A 401(k) match up to 7% alongside additional profit-sharing up to 10% materially enhances long-term compensation. These retirement features are highlighted as standout strengths of the overall package.
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Parental & Family Support — Generous paid parental leave (16 weeks maternity, 12 weeks parental), backup dependent care, and adoption assistance provide robust family support. Hybrid work and caregiving resources further ease family responsibilities.
Fidelity Investments Insights
What We Do
At Fidelity, our goal is to make financial expertise broadly accessible and effective in helping people live the lives they want. We do this by focusing on a diverse set of customers: - from 23 million people investing their life savings, to 20,000 businesses managing their employee benefits to 10,000 advisors needing innovative technology to invest their clients’ money. We offer investment management, retirement planning, portfolio guidance, brokerage, and many other financial products. Privately held for nearly 70 years, we’ve always believed by providing investors with access to the information and expertise, we can help them achieve better results. That’s been our approach- innovative yet personal, compassionate yet responsible, grounded by a tireless work ethic—it is the heart of the Fidelity way.
