AI​/ML Manager - Engineering Leader

About Articul8 AI

At Articul8, we build enterprise-grade Generative AI solutions that help global organizations unlock new value from their data. Our platform is trusted by some of the world's most innovative companies, and we partner closely with customers to design, deploy, and scale AI solutions that deliver measurable impact.

As a fast-moving startup, we move with urgency and focus. Every team member has real ownership, and the work you do here directly shapes our platform and our customers' success. If you thrive in an environment where innovation happens daily and impact is visible, Articul8 is the place to do the best work of your career.

Role Overview

As an AI/ML Manager, you will leverage your strong technical expertise in machine learning with your leadership skills to guide a high-performing team, shape the technical roadmap, and deliver impactful solutions across our products. This role blends technical leadership, applied research translation, and cross-functional collaboration to deliver impactful scalable solutions to our customers. You will shape the AI roadmap, cultivate a culture of excellence, and ensure scalable, data-driven execution in a fast-paced environment.

This role will engage directly with customers to understand their requirements and collaborate closely with multiple teams at Articul8 to translate these needs into actionable tasks for your team. You will oversee feature life cycles from ideation through deployment in production-grade code.

Candidates must have experience leading teams of both junior and senior engineers across multiple parallel projects in a fast-paced environment. This role also requires technical expertise in AI/ML, MLOps, cloud platforms, and coding best practices.

Key Responsibilities

• Lead, mentor, and grow a high-performing team of AI/ML engineers, fostering a culture of innovation, technical excellence, and continuous learning.

• Collaborate cross-functionally with Customer Success, Product Management, Engineering, and Business Development to scope, prioritize, and align AI/ML initiatives with core business objectives.

• Define and enforce best practices for the full ML lifecycle, including experimentation, code reviews, reproducibility, deployment pipelines, monitoring, and MLOps.

• Own the technical roadmap for AI/ML capabilities, ensuring alignment with long-term product strategy while rapidly adapting to research findings and market shifts.

• Drive translation of applied research into production-ready solutions, balancing cutting-edge innovation with pragmatic delivery at startup speed.

• Establish team processes for prioritization, planning, and technical guidance to optimize execution speed while ensuring reliability, scalability, and quality.

• Promote a data-driven culture by defining success metrics and KPIs, ensuring technical outputs are measurable, impactful, and tied to business outcomes.

• Contribute hands-on to technical architecture, model design, and code reviews where appropriate, while balancing technical leadership and management responsibilities.

• Advocate for responsible and ethical AI practices, ensuring compliance with organizational policies and industry standards.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related technical field;
  PhD preferred for deeper research leadership.

• Excellent communication and collaboration skills for cross-functional teamwork and translating technical AI/ML concepts into business impact.

• Strong organizational skills with proven experience managing multiple complex AI/ML projects and priorities in dynamic environments.

• 5+ years of professional experience in software engineering, machine learning, or applied AI, including at least 3 years in a leadership or management capacity.

• Proven track record successfully building, deploying, and scaling machine learning systems in production environments.

• Deep understanding of modern ML/AI techniques (e.g., deep learning, transformers, reinforcement learning) and proficiency with frameworks such as Tensor Flow, PyTorch.

• Experience with cloud platforms (AWS, GCP, Azure) and MLOps best practices/tools for model orchestration,…