Overview

Who We Are & Why Join Us

Responsibilities

• 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
• 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

Qualifications

• Familiarity with cloud platforms such as AWS, GCP, or Azure
• Strong software engineering fundamentals including system design, debugging, performance optimization, and production reliability
• 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
• 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

Nice to have

• 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
• Basic understanding of MLOps practices and model lifecycle management