Senior AI Engineer - Product Engineering

Location: Bengaluru, Karnataka, India

Department: Engineering

Work Location: Bangalore

Five Days onsite

Senior AI Engineer – Generative AI & LLMs

At Avathon, we are building cutting-edge AI solutions that transform operations across asset-intensive industries such as Supply Chain, Logistics, Energy, Mining, Aerospace, and Industrial Manufacturing. As an 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 across forecasting, route optimization, anomaly detection, predictive maintenance, and intelligent automation.

With 3–6 years of hands-on industry experience, you are expected to bring expertise in AI system design, ML engineering, LLM deployment, and scalable software development within fast-paced startup environments.

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
• Stay current with the latest advancements in LLMs, agentic systems, foundation models, and applied AI engineering

You'll Have

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field
• 3–6 years of hands-on industry experience in AI Engineering, Machine Learning Engineering, Applied AI, or related roles
• Strong experience building and deploying LLM-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 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
• Experience with containerization, deployment pipelines, and collaborative engineering environments
• Strong analytical thinking, ownership mindset, and ability to work in ambiguous, fast-moving startup environments
• Strong communication skills and ability to work cross-functionally with technical and business stakeholder

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 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
• Basic understanding of MLOps practices and model lifecycle management
• Experience working on applied AI projects in academic, internship, or startup settings
• Interest in industrial AI and asset-intensive environments
• Industry exposure in one or more of the following domains: Mining, Oil & Gas, Aerospace, Supply Chain, Logistics, or Renewable Energy

Interview Process

As part of the interview process, you will be asked to complete a technical case study assessment. Expect atleast two-three rounds of Technical interviewing rounds and one onsite round.