Senior AssociateManager Applied AI Engineer, Technology Consulting

Were hiring an Applied AI Engineer to own the AI stack end-to-end from the cloud infrastructure it runs on through the model layer to the systems and evaluations that keep it working in production. This is a builder role that flexes with the situation: some weeks youre heads-down shipping as an individual contributor others youre setting architectural direction and leading a small team. We want someone who can do both well and read which the moment calls for.

Youll work across the full model landscape. That means getting the most out of frontier APIs (Claude GPT and their peers) and deploying fine-tuning and operating open-source models when thats the right call on cost latency control or privacy. Knowing which to reach for and why is a core part of the job.

Were looking for someone who can apply first principles about why a transformer produces the output it does drive real results out of frontier models run open source models in production and design the cloud infrastructure to serve them reliably and cost-effectively at scale.

The opportunity:

• Own the architecture of production AI systems inference stacks fine-tuning pipelines retrieval and evaluation infrastructure monitoring
• Build on frontier models (Claude GPT and peers) with real rigor: tool use structured outputs context and cost management evals and guardrails not just prompt-and pray
• Deploy and operate open-source models (Llama Qwen Mistral DeepSeek and whatever comes next) on our cloud environment including quantization serving frameworks (vLLM TGI SGLang TensorRT-LLM) and multi-GPU inference.
• Make the frontier-vs-open-source call deliberately on cost latency control and data sensitivity grounds and be able to defend it
• Design the cloud infrastructure underneath it all: GPU orchestration autoscaling cost controls VPC/networking IAM observability. This is not a hand it to DevOps role
• Fine-tune distill and evaluate models against real task metrics not vibes not leaderboards.
• Track the current research literature (arXiv major labs key conferences) and make judgment calls on whats ready for production versus whats still noise
• Depending on the project contribute independently as a senior IC or lead and mentor a small team and switch between the two as needed
• Partner with product and leadership to translate ambiguous problems into systems that actually ship

To qualify for the role you must have:

Cloud & infrastructure engineering

• 6 years of software / infrastructure engineering with deep production experience on at least one major cloud (AWS GCP or Azure).
• Strong command of GPU infrastructure: instance selection driver / CUDA stack containerization Kubernetes or an equivalent orchestrator autoscaling patterns for inference workloads.
• IaC discipline (Terraform / Pulumi / CDK) CI/CD monitoring (Prometheus / Grafana / OpenTelemetry) and cost management as second nature.
• Fluent in Python; comfortable in at least one systems-adjacent language (Go Rust C) for the parts that need it.

ML / AI depth

• Genuine understanding of transformer internals: attention (including variants like MQA GQA sliding-window and flash attention) positional encodings (RoPE ALiBi) tokenization KV cache mechanics sampling and where each part contributes to latency memory and quality
• Proven ability to get production-grade results from frontier models Claude GPT and peers including tool use / function calling structured outputs retrieval context and cost management and building the evals and guardrails around them.
• Hands-on experience fine-tuning open-source LLMs full fine-tuning LoRA / QLoRA preference optimization (DPO / ORPO / equivalents) and knowing when each is appropriate.
• Practical familiarity with the modern training and inference stack: PyTorch Hugging Face DeepSpeed or FSDP vLLM or equivalent evaluation frameworks
• Ability to read a recent paper explain whats actually new and give an honest assessment of whether its worth integrating.
• Track record of taking models frontier or open weights into a production system that real users depend on with the evaluation guardrails and monitoring to match.

What we look for:

• Strong understanding of the mechanics before reaching for an abstraction.
• Able to contribute as an individual contributor adapting to the needs of the situation
• Communicate tradeoffs clearly to non-technical stakeholders