Artificial Intelligence / Machine Learning Architect

Role Overview

We are seeking a technically exceptional Associate to lead and drive a portfolio of advanced AI/ML initiatives, including AI red teaming, forward-deployed engineering engagements, and small language model (SLM) training. This is a hands-on leadership role for someone who combines deep technical expertise with the judgment to set direction, own engineering excellence, and mentor high-performing teams. The successful candidate will hold a PhD in Data Science, Machine Learning, AI, or a closely related field, and bring a strong understanding of the full lifecycle of AI workloads from data and training through deployment, dynamic routing, and cost governance.

Key Responsibilities

Technical & Engineering Leadership

• Set the technical vision and roadmap across red teaming, forward-deployed engineering, and SLM training workstreams.
• Own engineering excellence across teams: establish and enforce standards for code quality, testing, review, reproducibility, and documentation.
• Serve as the senior technical authority, making architecture and methodology decisions and reviewing critical work.

MLOps & AI Workloads

• Provide oversight of MLOps practice — training and inference pipelines, CI/CD for models, experiment tracking, model registry, versioning, and automated evaluation.
• Bring a deep understanding of AI workloads and their infrastructure demands: compute and GPU/accelerator utilization, distributed training, data pipelines, memory and throughput constraints, and cost/performance trade-offs.
• Drive reliability, observability, and monitoring of models in production, including drift detection and performance regression.

Dynamic Routing & Model Orchestration

• Design and oversee dynamic routing strategies across models and endpoints — directing requests to the most appropriate model based on complexity, latency, cost, and quality, including SLM/LLM tiering and fallback logic.
• Establish orchestration patterns that balance performance and efficiency across heterogeneous AI workloads.

AI FinOps & Cost Governance

• Own AI FinOps: monitor, forecast, and optimize the cost of training and inference across cloud and accelerator spend.
• Introduce cost-attribution, budgeting, and unit-economics practices (e.g. cost per token, per request, per workload) and drive efficiency initiatives without compromising quality or reliability.

AI Red Teaming

• Lead adversarial testing programs to probe model robustness, safety, and security across a range of AI systems.
• Design evaluation frameworks and threat models; translate findings into concrete mitigations and reporting for stakeholders.

Forward-Deployed Engineering

• Oversee engineers embedded with clients or partner teams, ensuring solutions are delivered to production standard in real-world environments.
• Balance rapid iteration with reliability, and act as the escalation point for the toughest technical problems on the ground.

SLM Training

• Drive the design, training, fine-tuning, and evaluation of small language models, including data curation, training pipelines, and optimization for efficiency and target performance.
• Establish reproducible, well-documented, and cost-aware training practices.

People & Delivery Management

• Line-manage and mentor a multidisciplinary team of engineers and researchers, raising the technical and engineering bar across the group.
• Own project planning, resourcing, and delivery across concurrent initiatives; report progress and risk to senior leadership.
• Partner with product, security, infrastructure, and client-facing teams to align technical work with business objectives.

Essential Qualifications & Experience

• PhD in Data Science, Machine Learning, AI, Computer Science, or a closely related quantitative discipline.
• Substantial hands-on experience training, fine-tuning, and evaluating machine learning models, including language models.
• Demonstrated technical leadership on complex, multi-stream AI/ML projects, with clear ownership of engineering excellence.
• Strong software engineering fundamentals and fluency in Python and modern ML frameworks (e.g. PyTorch, JAX, Hugging Face).
• Proven MLOps experience: building and operating training/inference pipelines, CI/CD for ML, experiment tracking, and model monitoring in production.
• Solid understanding of AI workloads and the underlying compute/infrastructure (GPUs/accelerators, distributed training, containerization, orchestration such as Kubernetes, and cloud ML platforms).
• Experience with dynamic model routing/orchestration and with AI FinOps or cloud cost optimization for ML workloads.
• Excellent communication skills, able to translate deep technical content for both expert and non-expert audiences.

Desirable

• Experience in AI safety, adversarial ML, or red teaming.
• Track record with small/efficient language models and resource-constrained training or inference.
• Experience in forward-deployed, consulting, or embedded-engineering models of delivery.
• Publications, patents, or recognized contributions to the AI/ML community.