AI Engineer – Generative AI & LLMs (Senior)

About the role

Avathon is building an industrial AI autonomy platform that powers predictive intelligence and agentic decision‑making for heavy‑industry customers. As a Senior AI Engineer you will design, develop, and deploy production‑grade Generative AI and Large Language Model solutions that address real‑world challenges such as forecasting, route optimisation, anomaly detection and predictive maintenance.

Key responsibilities

• Design, build and launch scalable AI/ML systems with a focus on Generative AI and LLM‑powered applications.
• Develop end‑to‑end LLM pipelines, including Retrieval‑Augmented Generation, fine‑tuning, prompt orchestration, evaluation and observability.
• Implement transformer‑based models and GenAI workflows for enterprise use cases.
• Create vector search, embedding pipelines and retrieval frameworks for knowledge‑intensive applications.
• Build robust backend services and APIs following modern engineering best practices.
• Collaborate with product, engineering and business teams to translate operational problems into AI solutions.
• Drive experimentation, benchmarking, performance optimisation and responsible AI practices.
• Contribute to MLOps workflows, CI/CD pipelines, model lifecycle management and cloud deployment.

Required profile

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science or a related field.
• Hands‑on industry experience in AI engineering, ML engineering or applied AI.
• Proven experience building and deploying LLM‑based applications in production.
• Strong software engineering fundamentals, system design, debugging and performance optimisation.
• Ability to work autonomously in a fast‑moving startup environment.

Required skills

• Python programming.
• PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex.
• Transformer architectures, LLM fine‑tuning, prompt engineering, RAG systems.
• Vector databases and embedding‑based search.
• Cloud platforms (AWS, GCP, Azure).
• Containerisation (Docker/Kubernetes) and CI/CD pipelines.
• API development and backend services.
• MLOps practices and model lifecycle management.