Senior AI/ML Engineer
At a glance
Senior Machine Learning Engineering role at Fidelity. Westlake · full-time · no salary stated · we estimate $181,150–$247,500 across the United States.
Fidelity has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build batch and real-time pipelines that ingest, transform, and deliver data to analytics and AI systems
- Create resilient connectors and embedding workflows, with parallel processing and safeguards for data integrity
- Deploy and run AI, LLM, agent, and retrieval services, then track quality, latency, drift, and alerts
- Improve AWS and Snowflake data workflows, including indexing, partitioning, and efficient resource use
- Write tested Python, support CI/CD releases, and prepare runbooks and incident response for production systems
What you bring
- Strong Python skills for production data and AI systems, with testing and maintainable code
- Strong SQL and Snowflake experience for analytics and data pipelines
- Hands-on experience with AWS services and production data pipelines using Spark, Airflow, or streaming tools
- Experience deploying and monitoring AI or LLM services in containers, plus CI/CD and infrastructure-as-code practices
Who this fits
This is a hands-on senior engineering role focused on production data and AI systems rather than data science. You’ll suit work that combines pipeline engineering, software delivery, and operational support. Fidelity will not sponsor immigration for this position. Onsite expectations vary by role and location and may change as the company phases in its onsite model.
From the employer
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.
Certifications:
Category:
Information TechnologyPlease 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.