Avathon is the leading Industrial AI autonomy platform, helping customers across heavy industries - energy, mining, manufacturing, aerospace, defense, and logistics - accelerate the journey toward autonomous operations. Our platform is built on a Computational Knowledge Graph foundation that contextualizes and connects operational data across siloed systems, bringing together time series, structured, unstructured, and machine vision data to power AI-driven applications in asset performance management, supply chain intelligence, visual AI, and global trade management.

- Cutting-Edge AI Innovation : Join a team at the forefront of AI, developing groundbreaking solutions that shape the future.

- High-Growth Environment : Thrive in a fast-scaling startup where agility, collaboration, and rapid professional growth are the norm.

- Meaningful Impact : Work on AI-driven projects that drive real change across industries and improve lives.

About the Role :

As a Senior AI Engineer, you will play a critical role in designing, developing, and deploying scalable AI systems with a strong focus on Generative AI, Large Language Models (LLMs), and production-grade machine learning applications. This role is ideal for someone with strong engineering depth who can bridge research and production-building robust AI platforms, optimizing LLM workflows, and delivering high-impact solutions.

You Will :

- Design, build, and deploy production-grade AI/ML systems with strong emphasis on Generative AI and LLM-powered applications.

- Develop and optimize end-to-end LLM pipelines including RAG architectures, fine-tuning, prompt orchestration, evaluation, and observability.

- Build scalable backend services and APIs for AI applications using modern engineering best practices.

- Implement and productionize transformer-based models and GenAI workflows for enterprise use cases.

- Design vector search systems, embedding pipelines, and retrieval frameworks for knowledge-intensive applications.

- Partner closely with Product, Engineering, and Business teams to translate operational challenges into scalable AI solutions.

- Drive experimentation, benchmarking, model evaluation, and performance optimization with scientific rigor.

- Improve inference efficiency, latency optimization, cost management, and reliability of deployed AI systems.

- Establish guardrails, hallucination detection, monitoring, and responsible AI practices for production deployments.

- Contribute to MLOps workflows including CI/CD, model lifecycle management, observability, and cloud deployment.

You'll Have :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.

- Hands-on industry experience in AI Engineering, Machine Learning Engineering, Applied AI, or related roles.

- Strong experience building and deploying LLM/SLM-based applications in production environments.

- Solid expertise with Python and modern AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or similar.

- Strong understanding of transformer architectures, LLM/SLM fine-tuning, prompt engineering, RAG systems, and vector databases.

- Experience building scalable APIs and backend systems supporting AI workflows.

- Familiarity with cloud platforms such as AWS, GCP, or Azure.

- Strong software engineering fundamentals including system design, debugging, performance optimization, and production reliability.

Preferred Qualifications :

- Exposure to Retrieval-Augmented Generation (RAG), vector databases, or embedding-based search systems.

- Familiarity with LLM observability and evaluation tools (e.g., Langfuse, LangSmith, Arize Phoenix, Weights & Biases).

- Hands-on experience with practical LLM/SLM deployment - prompt versioning, cost/latency tracking, guardrails, or hallucination detection.

- Exposure to LLM evaluation frameworks (e.g., RAGAS, DeepEval) or LLM-as-judge evaluation patterns.

- Industry exposure in one or more of the following domains : Mining, Oil & Gas, Aerospace, Supply Chain, Logistics, or Renewable Energy.