Senior Machine Learning Engineer

Capital Group•Los Angeles, CA

23h•$201,683 - $322,693•Hybrid

About The Position

You will join our Machine Learning Engineering team to build the next generation of AI products at Capital Group — including agentic systems, LLM-powered workflows, and the platform that ensures they are safe, governed, and reliable in production. You will operate at the intersection of production ML, GenAI and agentic workflows, and governed data infrastructure. In this high-impact role, you will help define how enterprise-grade AI systems are designed, deployed, and operated. You will work with a high degree of autonomy, mentor junior engineers, and drive engineering standards across projects built on Databricks, AWS, and agent-based architectures.

Requirements

7+ years of professional software engineering with strong proficiency in Python and core software engineering fundamentals.
Experience building and operating production ML systems end-to-end, including deployment, monitoring, and lifecycle management.
Hands-on experience with AWS and/or Databricks, including native AI/ML capabilities and Infrastructure as Code.
Experience integrating GenAI and LLMs using advanced prompt engineering and evaluation techniques.
Experience developing APIs (REST and streaming endpoints) and familiarity with MCP (Model Context Protocol).
Strong ML fundamentals, including algorithms, evaluation metrics, and model tuning.
Bachelor’s degree in information technology, computer science, or a related field.
Experience with data handling, including data cleaning, feature engineering, preprocessing, and exploratory data analysis.
Demonstrate the ability to operate autonomously on complex technical initiatives.
Experience with CI/CD and DevOps, including containerization, deployment pipelines, and testing frameworks.

Nice To Haves

Experience with agentic architectures, including multi-step reasoning, orchestration frameworks, tool/function calling, and agent evaluation.
Experience with data infrastructure for AI, including vector stores, graph databases, Redis, DynamoDB, and ElastiCache.
Experience with data governance tools and practices such as Unity Catalog, data lineage, access controls, and audit trails.
Experience with distributed computing, including Spark and large-scale data processing.
Experience designing human-in-the-loop systems, including guardrails, LLM output evaluation, and responsible AI practices.

Responsibilities

AI Infrastructure & Production Systems: Architect and operate end-to-end production AI systems — designing, building, deploying, monitoring, and managing the full lifecycle of ML and GenAI workloads.
Develop production-grade cloud-native environments optimized for AI/ML model training, serving, and orchestration.
Establish and evangelize engineering standards, reference patterns, and reusable platform components for AI services across the firm.
Design scalable inference pipelines, including retraining loops, drift detection, evaluation harnesses, and observability.
Agentic Workflows & GenAI: Build agentic systems with multi-step reasoning, orchestration, and tool/function calling, including MCP-based integrations.
Develop evaluation harnesses, traces, and replay tooling so agent behavior is observable and continuously improvable.
Apply advanced prompt engineering, evaluation frameworks, guardrails, and human-in-the-loop patterns to deliver reliable LLM-powered features.
Drive the agentic SDLC, defining how agents are designed, tested, evaluated, deployed, and monitored as first-class production assets.
Databricks & AWS Platform Engineering: Build solutions on Databricks (Unity Catalog, MLflow, Spark) and AWS, leveraging native AI capabilities for model training, serving, and governance.
Use Infrastructure as Code to provision and manage cloud-native, scalable, and secure environments.
Integrate with vector stores, graph databases, Redis, DynamoDB, and ElastiCache to enable retrieval, memory, and state for AI applications.
ML Engineering & Delivery: Build REST and streaming APIs to expose ML and agentic capabilities to downstream products and platforms.
Apply advanced prompt engineering, RAG patterns, fine-tuning, and model selection aligned to specific use cases.
Optimize performance, cost, and computational efficiency across distributed compute workloads.
Develop and tune ML models and perform data cleaning, feature engineering, preprocessing, and exploratory analysis.
Governance, Risk & Collaboration: Embed data lineage, access controls, audit trails, and responsible AI practices into every system you build.
Partner with product, business, and data teams to translate ambiguous problems into well-scoped agentic solutions.
Lead code reviews, set engineering standards, mentor junior engineers, and propose scalable solutions.