Senior AI/ML Engineer

Fidelity Investments•Westlake, TX

About The Position

Fidelity is seeking a hands-on Senior Engineer working at the intersection of data engineering and AI/ML/LLM engineering. The role spans the end-to-end lifecycle: building robust real-time and batch data pipelines that feed AI systems, then deploying and operating AI/ML/LLM and retrieval systems at scale. It is an engineering position, not a data science role. The engineer will build and operate AWS- and Snowflake-based data and deployment solutions, productionize AI/ML/LLM services, and promote reliability through testing, CI/CD, monitoring, observability, and operational support. The role is part of an advanced analytics/insights team responsible for data engineering, advanced analytics/insights, and production AI/ML/LLM engineering for HR technologies. Fidelity is transitioning to a full-time onsite working model through a phased rollout. Current onsite expectations vary by role and location and may evolve; this transition does not apply to fully remote roles. Fidelity will not provide immigration sponsorship for this position.

Requirements

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 such as EC2, S3, Lambda, Step Functions, SageMaker, IAM, and RDS.
Experience building and operating data pipelines using Spark, Airflow, or streaming frameworks such as Kafka or Kinesis.
Experience deploying and monitoring AI/ML/LLM services in containerized environments such as Docker and Kubernetes.
Experience with CI/CD and Infrastructure as Code, such as Terraform, CloudFormation, or similar.
Comfort debugging complex distributed systems using evidence-driven approaches.
Experience with vector databases and retrieval-augmented generation (RAG) patterns.
Familiarity with LLM frameworks such as LangChain or LlamaIndex 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.

Responsibilities

Design and build real-time and batch data pipelines for ingestion, transformation, streaming, and delivery to analytics and AI/ML systems.
Build resilient data connectors, chunking and 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, including retrieval quality metrics, latency tracking, drift detection, and alerting.
Translate prototypes into pragmatic, hardware-efficient, production-grade systems that are simple to operate.
Critically evaluate open-source AI/ML frameworks and adopt production-ready tools or replace unnecessary complexity with 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 development, UAT, and production environments.
Contribute to operational readiness through runbooks, incident response, and production support.
Raise technical standards through direct execution and engineering excellence.