AI Systems Engineer

AI Systems Engineer

Join the AI Studio of an innovative construction industry client in Boston as an AI Systems Engineer, a hybrid role responsible for architecting and building both:

1. The distributed systems backbone that powers enterprise-scale AI, and
2. The agentic and LLM-driven capabilities transforming construction workflows

This role sits at the intersection of platform engineering and applied AI. You will design scalable APIs, event-driven services, and reliable infrastructure — while also implementing multi-model AI agents, retrieval pipelines, and AI orchestration frameworks that operate in real-world production environments. You will help define how AI is built, deployed, observed, and scaled across the client's national operations.

Responsibilities:

• Design and implement production-grade RAG architectures
• Build and deploy multi-model AI agents leveraging AWS Bedrock and LLM providers (Claude, GPT, Llama, Titan, etc.)
• Implement dynamic model routing strategies based on task complexity, cost, and latency
• Develop multi-agent orchestration frameworks enabling collaborative workflows (planner, retriever, executor, summarizer)
• Design safe tool invocation patterns and guardrails for enterprise AI agents
• Optimize inference pipelines for cost, performance, and reliability
• Implement evaluation frameworks to measure model performance, hallucination rates, and response quality
• Design fallback and degradation strategies for model outages or latency spikes

• Architect and evolve service-oriented and event-driven systems supporting AI workloads
• Design REST/GraphQL APIs with clear versioning, authentication, and backward compatibility strategies
• Implement asynchronous processing pipelines using queues, event buses, and workflow orchestration
• Ensure reliability through idempotent consumers, retry strategies, circuit breakers, and dead-letter queues
• Make informed tradeoffs between relational, NoSQL, and vector storage systems
• Build services that are observable, traceable, and production-ready
• Define and document architectural standards for AI platform services
• Implement LLMOps: cost monitoring, latency optimization, usage analytics, and model versioning
• Enforce security, governance, and access standards in line with enterprise policies

• Work closely with product managers, site AI engineers, and data scientists to iterate rapidly in Agile sprints
• Communicate technical progress clearly to non-technical stakeholders; contribute to internal AI playbooks and templates

Qualifications:

• 6+ years of professional software engineering experience (not including vibe coding)
• Demonstrated experience designing distributed or service-oriented systems in production
• Strong backend engineering skills in Python, and at least one of Java, NodeJS, Rust or Kotlin
• Experience building and deploying event-driven architectures (SNS/SQS, Kafka, EventBridge, etc.)
• Experience integrating LLMs into production systems (Bedrock, OpenAI, Anthropic, etc.)
• Hands-on experience with RAG pipelines, vector databases and building multi-agent AI systems
• Deep understanding of:
• Distributed system failure modes
• API lifecycle management
• Concurrency and consistency tradeoffs
• LLM cost, latency, and reliability constraints
• Tuning AI Agents for accuracy and performance

Preferred:

• Experience building internal AI platforms or shared infrastructure
• Exposure to large-scale SaaS or mission-critical systems
• Experience designing multi-agent or orchestration frameworks
• Experience with Databricks Lakehouse architecture
• Prior experience in construction, manufacturing, or operational industries