Senior AI Engineer

Who We Are Why Join Us

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. With capabilities spanning digital twins, normal behavior modeling, natural language processing, and computer vision, Avathon delivers real-time predictive intelligence and agentic decision-making at industrial scale.

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.

Learn more at: avathon.com

About the Role

As a Senior AI Engineer at Avathon, you will play a key role in designing and delivering advanced AI solutions with a strong emphasis on Generative AI and Large Language Models (LLMs). You will apply scientific rigor to develop scalable, production-ready machine learning systems that drive measurable business impact, working on challenging problems in forecasting, demand planning, renewable energy optimization, anomaly detection, and prescriptive maintenance. With minimum 5 years of industry experience, you are expected to bring strong expertise in statistical modeling, ML engineering, and modern AI architectures, particularly in GenAI and LLM-based applications. This role offers the opportunity to work on high-impact projects that shape next-generation AI capabilities within the organization.

You Will

Design, develop, and deploy machine learning and Generative AI solutions to solve complex business problems

Build, fine-tune, and optimize Large Language Models (LLMs) and transformer-based architectures for real-world applications

Apply rigorous scientific methodologies to experimentation, model evaluation, and performance optimization

Develop scalable ML pipelines and production-grade systems in collaboration with Engineering teams

Conduct prompt engineering, model alignment, evaluation, and performance benchmarking for GenAI applications

Work closely with Product, Engineering, and Business stakeholders to translate ambiguous requirements into data-driven AI solutions

Instrument and monitor LLM applications in production using observability tools, tracking cost, latency, quality, and drift

Contribute to model governance, responsible AI practices, and performance monitoring in production environments

Stay current with advancements in Generative AI, LLM research, and applied machine learning, incorporating relevant innovations into company solutions

You'll Have

Master’s or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field

Minimum 5 years of hands-on industry experience in AI engineering, machine learning, data science, or applied AI roles

Strong experience with Generative AI frameworks and Large Language Models (e.g., transformer architectures, fine-tuning, RAG systems)

Proficiency in Python and modern ML/AI libraries such as PyTorch, TensorFlow, Hugging Face, or equivalent ecosystems

Solid understanding of statistical modeling, experimentation, and model evaluation methodologies

Experience building and deploying ML models into production environments

Familiarity with data engineering workflows and cloud-based ML platforms (AWS, GCP, or Azure)

Strong problem-solving skills with the ability to work independently on complex and ambiguous problem statements

Excellent communication skills with the ability to present technical insights clearly to cross-functional stakeholders

Preferred Qualifications

Experience implementing Retrieval-Augmented Generation (RAG), vector databases, and embedding-based search systems

Hands-on experience with LLM observability platforms (e.g., Langfuse, LangSmith, Arize Phoenix, Weights Biases) for tracing, cost tracking, and quality monitoring in production

Experience with LLM evaluation frameworks (e.g., RAGAS, DeepEval) and evaluation patterns such as LLM-as-judge and automated regression testing

Practical experience deploying LLM applications with guardrails, prompt versioning, hallucination detection, and model drift monitoring

Exposure to distributed training, model optimization, and scalable inference architectures

Knowledge of MLOps practices, CI/CD for ML (Travis CI, Jenkins), and model lifecycle management

Prior experience applying AI solutions in industrial or asset-intensive enviro…