Senior Machine Learning Engineer

"I can be myself at work."

You are more than a job title. We want you to feel comfortable doing great work and bringing your best, authentic self to everything you do. We value your talents, traditions, and uniqueness-and we're committed to fostering a strong sense of belonging in a respectful workplace.

We intentionally seek diverse perspectives, experiences, and backgrounds, investing in a culture designed to celebrate differences. We believe that belonging leads to better outcomes and a stronger community of associates united by our mission. At Capital, we live our core values every day: Integrity, Client Focus, Diverse Perspectives, Long-Term Thinking, and Community.

"I can influence my income."

You want to feel recognized at work. Your performance will be reviewed annually, and your compensation will be designed to motivate and reward the value that you provide. You'll receive a competitive salary, bonuses and benefits. Your company-funded retirement contribution will factor in salary and variable pay, including bonuses.

"I can lead a full life."

You bring unique goals and interests to your job and your life. Whether you're raising a family, you're passionate about where you volunteer, or you want to explore different career paths, we'll give you the resources that can set you up for success.

• Enjoy generous time-away and health benefits from day one, with the opportunity for flexible work options
• Receive 2-for-1 matching gifts for your charitable contributions and the opportunity to secure annual grants for the organizations you love
• Access on-demand professional development resources that allow you to hone existing skills and learn new ones

• You architect and operate end-to-end production AI systems - designing, building, deploying, monitoring, and managing the full lifecycle of ML and GenAI workloads

• You develop production-grade cloud-native environments optimized for AI/ML model training, serving, and orchestration

• You establish and evangelize engineering standards, reference patterns, and reusable platform components for AI services across the firm

• You design scalable inference pipelines, including retraining loops, drift detection, evaluation harnesses, and observability

• You build agentic systems with multi-step reasoning, orchestration, and tool/function calling, including MCP-based integrations

• You develop evaluation harnesses, traces, and replay tooling so agent behavior is observable and continuously improvable

• You apply advanced prompt engineering, evaluation frameworks, guardrails, and human-in-the-loop patterns to deliver reliable LLM-powered features

• You drive the agentic SDLC, defining how agents are designed, tested, evaluated, deployed, and monitored as first-class production assets

• You build solutions on Databricks (Unity Catalog, MLflow, Spark) and AWS, leveraging native AI capabilities for model training, serving, and governance

• You use Infrastructure as Code to provision and manage cloud-native, scalable, and secure environments

• You integrate with vector stores, graph databases, Redis, DynamoDB, and ElastiCache to enable retrieval, memory, and state for AI applications

• You build REST and streaming APIs to expose ML and agentic capabilities to downstream products and platforms

• You apply advanced prompt engineering, RAG patterns, fine-tuning, and model selection aligned to specific use cases

• You optimize performance, cost, and computational efficiency across distributed compute workloads

• You develop and tune ML models and perform data cleaning, feature engineering, preprocessing, and exploratory analysis

• You embed data lineage, access controls, audit trails, and responsible AI practices into every system you build

• You partner with product, business, and data teams to translate ambiguous problems into well-scoped agentic solutions

• You lead code reviews, set engineering standards, mentor junior engineers, and propose scalable solutions

• You have 7+ years of professional software engineering with strong proficiency in Python and core software engineering fundamentals

• You have experience building and operating production ML systems end-to-end, including deployment, monitoring, and lifecycle management

• You have hands-on experience with AWS and/or Databricks, including native AI/ML capabilities and Infrastructure as Code

• You have experience integrating GenAI and LLMs using advanced prompt engineering and evaluation techniques

• You have experience developing APIs (REST and streaming endpoints) and familiarity with MCP (Model Context Protocol)

• You have strong ML fundamentals, including algorithms, evaluation metrics, and model tuning

• You have a bachelor's degree in information technology, computer science, or a related field.

• You have experience with data handling, including data cleaning, feature engineering, preprocessing, and exploratory data analysis

• You demonstrate the ability to operate autonomously on complex technical initiatives

• You have experience with CI/CD and DevOps, including containerization, deployment pipelines, and testing frameworks

• You have experience with agentic architectures, including multi-step reasoning, orchestration frameworks, tool/function calling, and agent evaluation

• You have experience with data infrastructure for AI, including vector stores, graph databases, Redis, DynamoDB, and ElastiCache

• You have experience with data governance tools and practices such as Unity Catalog, data lineage, access controls, and audit trails

• You have experience with distributed computing, including Spark and large-scale data processing

• You have experience designing human-in-the-loop systems, including guardrails, LLM output evaluation, and responsible AI practices