Toptal: Applied AI Engineers for telecommunications company

Summary

The Senior AI Professional will help design, develop, and optimize production-grade agentic AI solutions. This role complements an existing platform engineering team and focuses on turning AI concepts into reliable, scalable software rather than purely research-oriented work.

General information

This engagement requires deep hands-on expertise in machine learning and applied AI, with a strong emphasis on prompt optimization, model behavior, agent orchestration, and evaluation. The work will involve collaborating with platform engineers, product teams, and business stakeholders to improve AI quality, reliability, and performance in production environments.

Tasks and Deliverables

Design, develop, and optimize production-grade agentic AI solutions.

Run meaningful evaluations to improve prompts, model behavior, orchestration, reliability, and performance in production.

Required experience

• Deep expertise in machine learning and applied AI.
• Experience building and deploying production-level AI/ML services.
• Strong software engineering and coding skills.
• Experience developing or optimizing LLM- and agent-based applications.
• Strong knowledge of prompt engineering and optimization.
• Strong knowledge of model evaluation and benchmarking.
• Strong knowledge of AI quality, reliability, and performance measurement.
• Strong knowledge of agent behavior and orchestration.
• Ability to design and run meaningful evaluations and interpret results.
• Experience with APIs, data pipelines, backend systems, and AI platforms.
• Familiarity with modern AI tools, frameworks, and cloud environments.
• Ability to collaborate with platform engineers, product teams, and business stakeholders.
• Strong communication skills and experience delivering maintainable, production-quality solutions.

Nice to have:

• Python experience.
• Background as an Applied AI Scientist, AI Engineer, or Machine Learning Engineer.
• Engagement highlights
• Own a defined production AI workstream with direct impact on reliability and scale.
• Work closely with an existing platform engineering team on applied AI delivery.
• Focus on production-grade AI implementation rather than purely research-oriented work.