Senior AI Engineer

What you’ll do

Embed & deploy

Tackle greenfield problems alongside internal teams and customers — scope ambiguous needs and build agents from scratch that fit how they actually work.

Own deployments end-to-end: discovery, build, integration, activation, and the tuning that earns trust and adoption.

Lead pilots and demos, drive adoption, and clear blockers before they stall a rollout.

Build agentic systems

Architect agentic systems — reasoning, planning, tool use, memory, multi-agent coordination — that run real workflows with guardrails.

Build safe tool-use infrastructure across APIs, databases, and services, with permissioning, sandboxing, and human-in-the-loop.

Ship SDKs, patterns, and reusable blueprints so internal teams build and deploy agents fast.

Make it reliable

Design and run rigorous evals: measure quality, catch regressions, and feed results back into the system.

Build observability, tracing, and guardrails that prove agents are safe and keep them safe as models and data drift.

Own the multi-model inference your agents depend on (text, voice, code, vision) — latency, throughput, and cost.

Lead

Set technical direction and standards for agentic systems; mentor engineers and partner with ML, product, and security.

What you’ll bring

5+ years in software engineering, with 3+ in AI systems or LLM applications, and production systems shipped end-to-end.

Strong grasp of LLM agent architectures (ReAct, RAG, tool use, multi-agent) and hands-on agentic orchestration and evaluation.

Proficiency in Python across a broad stack — pipeline, agent, service, and instrumentation.

Production experience on AWS and Azure with containerized deployments (Docker, Kubernetes).

Strong customer and stakeholder instincts; able to impose structure on ambiguity and push back when needed.

A bias toward shipping and comfort operating without a clean spec.

Solid understanding of agentic security risks (prompt injection, privilege escalation, data leakage).

Strong written and verbal communication.

Nice to have

Agentic systems in regulated industries (fintech, payments, credit, healthcare).

Cloud AI/ML services (AWS SageMaker / Bedrock, Azure ML / Azure OpenAI); multi-cloud or hybrid.

MCP or agent communication standards; agent evaluation and observability tooling.

Model serving (vLLM, TensorRT-LLM, Triton), fine-tuning, quantization, or LoRA.

Workflow orchestration (Temporal, Airflow, Prefect) for AI workloads; voice / multimodal / edge inference.

Testing and verification for non-deterministic AI systems.