Overview

As an AI Engineer working with one of Proactivitis enterprise clients, you will help enhance productivity through AI-driven experiences and services across core business applications. The team builds foundational platforms and experiences that integrate large language models (LLMs) and real-time intelligence into end-user tools, enabling capabilities such as content generation, navigation, comprehension, and workflow automation.

This role focuses on designing and delivering scalable AI platforms that combine state-of-the-art LLMs with enterprise data and deep application integration. You will collaborate closely with software engineers, researchers, and product managers to translate product needs into well-defined machine learning problems, advance applied LLM capabilities, and bring solutions from prototype to large-scale production.

You will build the engineering systems that make AI reliable in production—developing APIs, platforms, and services around AI features; designing data pipelines and feedback loops; deploying and fine-tuning modern deep learning models; orchestrating prompts and tools; and monitoring AI-specific signals such as drift, hallucinations, safety, and cost alongside traditional reliability metrics.

This is an opportunity to apply cutting-edge AI to real-world, high-impact products used at scale.

Responsibilities

• Feature delivery and collaboration
  Partner with product management and engineering teams to co-own scenario goals and translate product requirements into scientific plans and production-ready solutions that meet quality, latency, and cost targets.
• Generative AI and advanced technologies
  Apply expertise in generative AI, large language models, and modern frameworks to build intelligent features, automation, and AI-powered services.
• Cloud platform integration
  Deploy, integrate, and operate AI-powered solutions within a cloud ecosystem, ensuring security, scalability, reliability, and compliance with best practices.
• Continuous learning and knowledge sharing
  Stay current with advances in generative AI and software engineering, propose improvements to development processes, and actively share knowledge with teammates.

Qualifications

Required Qualifications

• Bachelors degree in Computer Science or a related technical discipline, or equivalent practical experience
• Proven coding experience in one or more of the following languages: C, C++, C#, Java, JavaScript, Python, or similar
• AI and machine learning expertise
  Hands-on experience with generative AI or machine learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and a strong understanding of LLM concepts, embeddings, and prompt engineering
• Distributed systems and cloud experience
  Familiarity with cloud services, microservices architectures, and distributed storage systems (experience with globally distributed databases is a plus). Ability to design fault-tolerant, secure, and compliant systems at scale
• Security requirements
  Ability to meet client, customer, and/or government security screening requirements as applicable to the role

Preferred Qualifications

• 1+ years of experience designing and operating infrastructure systems that run at global scale
• Experience deploying AI models into production and building systems that monitor, evaluate, and retrain models automatically
• Strong interest in mentoring others and contributing to an inclusive, collaborative team culture