We are seeking an experienced AI / ML Developer with strong hands-on expertise in large language models (LLMs) and AI-driven application development. The ideal candidate will have practical experience working with GPT and Anthropic models, building and training B2B products powered by AI, and leveraging AI-assisted development tools to deliver scalable and intelligent solutions.
Responsibilities
- Analyse, customise, and optimise GPT and Anthropic-based models to ensure reliability, scalability, and performance for real-world business use cases.
- Design and build AI-powered products, including model training, fine-tuning, evaluation, and performance optimisation across development lifecycles.
- Develop and refine prompt engineering strategies to improve model accuracy, consistency, relevance, and contextual understanding.
- Build, integrate, and deploy AI services into applications using modern development practices, APIs, and scalable architectures.
- Leverage AI-enabled coding tools such as Cursor and GitHub Copilot to accelerate development, improve code quality, and enhance efficiency.
- Work closely with product, business, and engineering teams to translate business requirements into effective AI-driven solutions.
- Monitor model performance, analyse outputs, and iteratively improve accuracy, safety, and overall system effectiveness.
Requirements
- Hands-on experience analysing, developing, fine-tuning, and optimising GPT and Anthropic-based large language models.
- Strong expertise in prompt design, experimentation, and optimisation to enhance response accuracy and reliability.
- Proven experience building, training, and deploying chatbots or conversational AI systems.
- Practical experience using AI-assisted coding tools such as Cursor or GitHub Copilot in production environments.
- Solid programming experience in Python or similar languages, with strong problem-solving and development fundamentals.
- Experience working with embeddings, similarity search, and vector databases for retrieval-augmented generation (RAG).
- Knowledge of MLOps practices, including model deployment, versioning, monitoring, and lifecycle management.
- Exposure to cloud environments such as AWS or Azure for deploying and managing AI solutions.
- Familiarity with APIs, microservices architecture, and system integration for scalable AI applications.
This job was posted by Bhanu Sree from ProductNOVA.
