Senior AI Engineer

Job Details:

We are seeking a highly skilled and hands-on Senior AI Engineer to join a small, high-impact team building the AI foundation that powers the next generation of our platform, including multi-agent systems, ML scoring models, and LLM-powered products that serve millions of payers. You’ll design, build, and ship production AI that directly drives revenue and customer experience.

This is a hands-on builder role reporting to the AI Engineering Lead. You’ll own implementation of agentic pipelines, ML models, eval frameworks, and LLM integrations end-to-end on Azure using Python and .NET, and use AI-powered development tools like Claude Code and GitHub Copilot as daily force multipliers to iterate faster. This role is based in our Boston office, working hybrid with three days in-office per week.

Success Profile:

This role is anchored in our company’s core competencies—These competencies reflect the mindsets and behaviors that define success in this role. We outline how each competency translates into real-world actions and outcomes specific to this role.

Results Driven

• Owns end-to-end delivery of agentic pipelines, ML models, and LLM integrations on Azure, shipping a first production feature or pipeline contribution within the first 30 days.
• Builds end-to-end ML pipelines on Azure ML, covering feature engineering, model training for propensity, adoption, and anomaly detection use cases, offline batch scoring, and drift monitoring, to deliver measurable improvements in model accuracy and pipeline throughput.
• Designs and implements production multi-agent orchestration, including classification, routing, authentication, and domain-specialist agents that handle real financial transactions with high reliability.
• Delivers against 30- and 90-day milestones, including owning an end-to-end agent or ML pipeline in production, contributing to two or more design reviews, and demonstrating domain understanding of our billing and payments context.

Takes Ownership

• Owns implementation of agentic pipelines, ML models, eval frameworks, and LLM integrations end-to-end, taking full accountability from design through production deployment and monitoring.
• Manages model versioning across multiple vertical segments using Azure ML Model Registry, ensuring traceability and stability as models move through their lifecycle.
• Implements MCP (Model Context Protocol) tool integrations through API gateways with authentication gating and policy enforcement, taking ownership of secure, governed access to production systems.
• Works with Snowflake data to build feature stores and orchestrate scoring pipelines that write predictions back into production systems, owning the full data-to-decision pipeline.

Drives Efficiency

• Uses AI-accelerated development tools such as Claude Code, Cursor, and GitHub Copilot as daily force multipliers, delegating multi-step tasks to iterate three to five times faster.
• Establishes an AI-accelerated development workflow within the first 30 days, and becomes a recognized power user of these tools by 90 days, sharing patterns and best practices with the team.
• Implements token-level cost optimization and efficient prompt chains to keep production LLM workflows performant and cost-effective at scale.
• Builds production, API-first backend services in .NET/C# and manages containerized deployments through Azure DevOps CI/CD and Kubernetes/AKS, streamlining the path from code to production.

Innovative

• Integrates leading-edge LLMs, including GPT-4o and Claude, into production workflows and builds optimized RAG pipelines using Azure AI Search to keep our AI products at the forefront of the industry.
• Designs multi-agent architectures using Microsoft Agentic Framework and Semantic Kernel, exploring new patterns for agent collaboration, tool use, and orchestration.
• Fine-tunes models for domain-specific tasks and designs prompt chains with versioning and content safety guardrails, balancing innovation with responsible AI practices.
• Stays current on emerging AI/ML tooling, agent architectures, and eval frameworks, and brings new approaches back to the team to continuously raise the bar on how we build AI products.

Requirements

• Bachelor’s degree in Computer Science, AI/ML, Mathematics, or a related technical field, or equivalent practical experience.
• 5–8 years of experience in software engineering, including 3+ years in AI/ML, having shipped models and LLM features that real users depend on.
• Fluency in Python, including scikit-learn, PyTorch or TensorFlow, pandas, and the broader Python ML ecosystem.
• Experience with the Azure cloud platform, including Azure ML, AI Foundry (or OpenAI Service), AI Search, Cosmos DB, and AKS.
• Production experience with LLMs, including RAG pipelines, prompt chains, fine-tuned models, or agentic workflows.
• Active use of AI-accelerated development tools such as Claude Code, Cursor, or GitHub Copilot to delegate multi-step tasks and iterate faster.
• Production experience with modern .NET/C#, building API-first backend services, integrations, and cloud-native workloads.
• Hands-on experience with Azure DevOps CI/CD, containerized deployments, and Kubernetes/AKS production operations.

Nice to Have

• Experience in fintech, payments, billing, or financial services.
• Experience with multi-agent architectures such as Semantic Kernel, LangChain/LangGraph, CrewAI, A2A, or MCP.
• Familiarity with ML Ops and eval frameworks such as Galileo, Weights & Biases, or MLflow.
• Experience with omnichannel AI, including web chat, voice (STT/TTS), or SMS.
• .NET/C# experience for agent runtime work.