Why This Role Stands Out
This hybrid role offers a fantastic opportunity to design and deploy cutting-edge AI solutions, significantly impacting how global clients find talent, while continuously developing your technical and leadership skills. You'll thrive here if you're a seasoned AI developer passionate about building scalable ML models and LLM-driven features within a flexible, people-first environment. Apply now to shape the future of work and grow your career!
Quick Overview
Job Description
Aquent Talent seeks a Senior AI Developer to design and deploy cutting-edge AI solutions that power talent matching and digital experiences for global clients. You'll build scalable ML models, LLM- and NLP-driven features, and data pipelines that improve how companies find and engage creative and marketing talent. Partnering with cross-functional teams, you'll own projects from experimentation through production, mentor engineers, and establish best practices. Join a people-first, inclusive, flexible environment where you can shape the future of work and continuously grow your technical and leadership skills.
Responsibilities
- Design, build, and deploy advanced AI and machine learning solutions for Aquent Talent and its clients
- Develop scalable models and APIs that power talent matching, personalization, and workflow automation
- Partner with product, data, and engineering teams to translate business needs into AI-driven features
- Evaluate and implement state-of-the-art models (LLMs, NLP, recommendation systems, predictive analytics)
- Optimize models for performance, cost, and reliability in production environments
- Establish best practices for data pipelines, experimentation, and model lifecycle management
- Mentor junior engineers and contribute to architecture, code reviews, and technical standards
- Monitor AI systems in production, analyze results, and iterate based on performance and user feedback
Required Skills
- Python
- Machine learning engineering
- Deep learning frameworks (Tensor
- Flow/Py
- Torch)
- Natural language processing (NLP)
- Large Language Models (LLMs) and prompt engineering
- MLOps and model deployment (Docker, Kubernetes, CI/CD)
- Cloud platforms (AWS, GCP, or Azure)
- Data engineering (ETL, data pipelines)
- SQL and No
- SQL databases
- API design and microservices
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