AI Systems Engineer

Job Title: AI Systems Engineer

Location: Boston, MA – Onsite at Corporate Headquarters

Duration: Full‑Time Employee (FTE) (Can be contract to hire)

Client: Suffolk Construction

Overview:

Suffolk’s AI Studio is seeking AI Systems Engineers to design and build the enterprise‑scale systems that power applied AI across the organization. This role sits at the intersection of distributed systems engineering and production AI, focusing on scalable APIs, event‑driven services, and reliable AI infrastructure while also implementing agentic and LLM‑driven capabilities. Engineers in this role help define how AI is architected, deployed, governed, and scaled across Suffolk’s national operations.

Top Skills: (Must‑Haves)

Distributed systems and service‑oriented architecture experience

Strong backend engineering in Python and one additional language such as Java, NodeJS, Rust, or Kotlin

Production experience integrating LLMs (AWS Bedrock, OpenAI, Anthropic, etc.)

Hands‑on experience with RAG pipelines, vector databases, and agentic systems

Event‑driven architectures (Kafka, SNS/SQS, EventBridge or similar)

Strong understanding of reliability, observability, and system failure modes

What You’ll Be Doing:

Designing and implementing production‑grade RAG architectures and AI agent systems

Building multi‑model AI agents and orchestration frameworks that balance cost, latency, and accuracy

Architecting scalable APIs and event‑driven services to support AI workloads

Implementing reliability patterns including retries, idempotency, circuit breakers, and fallbacks

Defining and enforcing architectural standards for AI platform services

Implementing LLMOps practices such as cost monitoring, latency optimization, usage analytics, and model versioning

Ensuring AI systems meet enterprise security, governance, and access standards

Collaborating closely with product managers, AI engineers, site AI engineers, and data teams in Agile delivery

Nice to Have / Additional Notes:

Experience building shared AI platforms or internal developer infrastructure

Exposure to Databricks Lakehouse architecture

Background in construction, manufacturing, or operational industries

Comfort operating in a highly collaborative, fast‑moving enterprise AI environment