Senior Machine Learning Engineer

Job Summary

A Senior Machine Learning Engineer at Capital Group will build next‑generation AI products—agentic systems, LLM‑powered workflows, and a production‑grade platform that ensures safety, governance, and reliability.

• Architect and operate end‑to‑end production AI systems, including design, build, deployment, monitoring, and lifecycle management of ML and GenAI workloads.
• Develop cloud‑native environments optimized for model training, serving, and orchestration.
• Establish engineering standards, reference patterns, and reusable platform components for AI services across the firm.
• Design scalable inference pipelines with retraining loops, drift detection, evaluation harnesses, and observability.

• Build agentic systems with multi‑step reasoning, orchestration, and tool/function calling, including MCP‑based integrations.
• Develop evaluation harnesses, traces, and replay tooling to make agent behavior 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 design, testing, evaluation, deployment, and monitoring practices for agents as first‑class production assets.

• Build solutions on Databricks (Unity Catalog, MLflow, Spark) and AWS, leveraging native AI capabilities for 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 Elasti Cache to support retrieval, memory, and state for AI applications.

• 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 tailored to specific use cases.
• Optimize performance, cost, and computational efficiency across distributed compute workloads.
• Develop and tune ML models, perform data cleaning, feature engineering, preprocessing, and exploratory analysis.

• Embed data lineage, access controls, audit trails, and responsible AI practices into every system.
• 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.

• 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) 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.
• Ability to operate autonomously on complex technical initiatives.
• Experience with CI/CD and Dev Ops, including containerization, deployment pipelines, and testing frameworks.

• Experience with agentic architectures, including multi‑step reasoning, orchestration frameworks, tool/function calling, and agent evaluation.
• Experience with data infrastructure for AI, such as vector stores, graph databases, Redis, DynamoDB, and Elasti Cache.
• 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…