AI​/ML Engineer

Position: AI/ML Engineer

Location:
Scottsdale, AZ
* From Day 1 Onsite

We are seeking an experienced AIML Engineer to design, build, and operate AI/ML infrastructure and agentic systems. This role involves developing MCP servers and agents, integrating LLMs, and implementing RAG pipelines for production environments.

• Design, build, and operate MCP servers and agents that host, orchestrate, and monitor AI/agent workloads.
• Develop agentic AI, prompt engineering patterns, LLM integrations, and developer tooling for production use.
• Own deployment, scaling, reliability, and cost-efficiency on Kubernetes/Docker and Google Cloud with automated CI/CD.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines and integrations with vector stores and retrieval tooling; use Lang Chain and Langfuse for orchestration, chaining, and observability.

• Implement and maintain MCP server and agent code, APIs, and SDKs for model access and agent orchestration.
• Design agent behavior, workflows, and safety guards for agentic AI systems.
• Create, test, and iterate prompt templates, evaluation harnesses, and grounding/chain-of-thought strategies.
• Integrate LLMs and model providers (self-hosted and cloud APIs) with unified adapters and telemetry.
• Build developer tooling: CLI, local runner, simulators, and debugging tools for agents and prompts.
• Containerize services (Docker), manage orchestration (Kubernetes/GKE), and optimize nodes, autoscaling, and resource requests.
• Ensure observability: logging, metrics, traces, dashboards, alerting, and SLOs for model infra and agents.
• Create runbooks, playbooks, and incident response procedures; reduce MTTR and perform postmortems.
• Design and maintain RAG workflows: document chunking, embeddings, vector indexing, retrieval strategies, re-ranking, and context injection.
• Integrate and instrument Lang Chain for composable chains, agents, and tooling; use Langfuse (or equivalent tracing) to capture prompts, model calls, RAG traces, and evaluation telemetry.

• 5+ years of strong software engineering (Python/NodeJS), system design, and production service experience.
• 2+ years of experience with LLMs, prompt engineering, and agent frameworks.
• 2+ years of practical experience implementing RAG: embeddings, vector DBs, and retrieval tuning.
• 2+ years of experience with Lang Chain patterns and with toolchain telemetry (Langfuse or similar) for prompt/model traceability.
• 5+ years of experience with Kubernetes, Docker, CI/CD, and infrastructure-as-code.
• 2+ years of experience with Google Cloud Platform services.
• 2+ years of experience with observability, testing, and security best practices for distributed systems.
• 2+ years of experience with evaluating and mitigating retrieval/augmentation failures, hallucinations, and leakage risks in RAG systems.
• Familiarity with vendor and open-source vector stores and embedding providers.
• Familiarity with CI/CD pipelines (Jenkins, Git Hub Actions, Git Lab CI, or ArgoCD).