AI Engineering Lead

About the Company

Our client is fast-scaling, SaaS technology company that allows companies to processes large volumes of unstructured data to deliver automated classification, filtering, and decision-making workflows for SMB clients. We handle billions of events and require models and systems that are highly scalable, robust, and production-grade.

We are expanding our leadership and looking for a

AI Engineering Lead

who can own the technical direction, architecture, and productionization of our AI systems.

Role Overview

The

AI Engineering Lead

will be the technical leader responsible for designing, scaling, and delivering the company’s most critical AI systems.

This is a highly strategic and hands-on role: part architecture, part research, part engineering, and part mentorship. You will define how AI is built, deployed, monitored, and scaled across the entire product.

Key Responsibilities

1. Technical Leadership & Architecture Define the

AI system architecture

end-to-end (training pipelines, inference, real-time processing, monitoring, auto-scaling). Lead the design of distributed ML systems that support

massive scale production workloads

. Ensure best practices in MLOps, CI/CD for models, observability, safety, and reliability. Mentor and technically lead senior ML engineers, data scientists, and MLOps engineers.

2. Model Development Build and optimize

image classification models

(CNNs, ViTs, multimodal architectures). Develop

language control and censorship models

(toxicity detection, abuse filtering, safe text classification, LLM guardrails). Develop and improve

document and email classification models

, including layout-aware models, OCR pipelines, and multi-modal understanding.

3. AI for Large-Scale Production Own the strategy to

scale AI models in production

(distributed inference, caching strategies, GPU/CPU optimization, autoscaling). Guarantee low-latency inference, cost efficiency, and high-availability AI services. Drive continuous model improvement cycles with A/B testing, human feedback loops, and telemetry.

4. Collaboration & Cross-Functional Work Work closely with Product, Engineering, Data, and Security teams to align AI roadmap with business needs. Translate complex AI concepts into actionable plans for both technical and non-technical stakeholders. Collaborate on client-facing AI performance requirements and enterprise-grade SLAs.

5. AI Governance & Safety Lead the development of

AI safety frameworks

, guardrails, and content compliance. Ensure all models meet security, privacy, and ethical standards.

Required Qualifications

7+ years of experience in ML/AI engineering, with at least 3 years in senior/lead/principal roles. Demonstrated expertise in: Image Classification Model, Language Safety / Censorship / Toxicity / Content Moderation models. Document Classification

(emails, PDFs, structured/unstructured documents). Design of scalable AI production systems

(microservices, distributed inference, parallel training). Strong track record deploying AI models at scale (millions of daily requests). Solid background in

MLOps

, including feature stores, pipelines, orchestration, monitoring, and versioning. Strong communication skills and ability to lead technical discussions. Strategic mindset with hands-on execution capability. Ability to operate in fast-paced, high-growth technology environments. Degree in Computer Science, AI, Machine Learning, or related field. MSc or PhD preferred but not required if compensated by strong experience.