Machine Learning Engineer - Freelancer
Machine Learning Engineer - (Freelance) - Remote
**We are only able to accept applications from candidates who currently reside in Estonia, Poland or Slovakia, please only apply if you are in these locations.**
Overview:
An innovative and forward-thinking team is seeking an experienced Machine Learning Engineer to support the development of cutting-edge conversational AI systems. This freelance/contract opportunity is ideal for a skilled professional passionate about building intelligent interfaces powered by the latest in language models, retrieval-augmented generation (RAG), and agent-based AI systems. You’ll be working at the intersection of backend engineering and AI/ML, helping build advanced digital assistants that enhance both customer-facing and internal user experiences.
Key Responsibilities:
- Design and develop backend components for natural language understanding, dialogue management, and response generation.
- Implement and optimise AI/ML algorithms for scalability and efficiency.
- Integrate large language models and other ML components into conversational platforms.
- Monitor system performance and iterate on improvements based on data and feedback.
- Stay informed of emerging trends in NLP, LLMs, and ML infrastructure; incorporate relevant advances into the platform.
- Continuously enhance ML pipelines and operational workflows.
- Collaborate on code reviews and contribute to a high-standard engineering culture.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, Machine Learning, or a related technical discipline.
- 5+ years of experience in product development with a strong foundation in conversational AI and generative technologies.
- Demonstrated expertise in NLP, LLMs, and RAG-based architectures.
- Strong coding proficiency in Python and familiarity with ML ecosystems (e.g., LangChain, LlamaIndex, FastAPI, vector databases).
- Experience deploying and integrating machine learning models in real-world applications.
- Solid understanding of backend systems, APIs, microservices, and modern cloud infrastructure (e.g., AWS, Azure).
- Familiarity with model deployment best practices and MLOps tools.
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