Experience:

• 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
• Experience with containerization, deployment pipelines, and collaborative engineering environments
• Experience working on applied AI projects in academic, internship, or startup settings

Qualification:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field

Responsibilities:

• Design, build, and deploy production-grade AI/ML systems with 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 with Product, Engineering, and Business teams to translate operational challenges into scalable AI solutions
• Drive experimentation, benchmarking, model evaluation, and performance optimization
• 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 latest advancements in LLMs, agentic systems, foundation models, and applied AI engineering

Additional responsibilities:

• Strong communication skills and ability to work cross-functionally with technical and business stakeholders
• Strong analytical thinking and ownership mindset
• Ability to work in ambiguous, fast-moving startup environments

Nice to have:

• Exposure to Retrieval-Augmented Generation (RAG), vector databases, or embedding-based search systems
• Familiarity with LLM observability and evaluation tools such as Langfuse, LangSmith, Arize Phoenix, Weights & Biases
• Hands-on experience with practical LLM/SLM deployment including prompt versioning, cost/latency tracking, guardrails, or hallucination detection
• Exposure to LLM evaluation frameworks like RAGAS, DeepEval or LLM-as-judge evaluation patterns
• Basic understanding of MLOps practices and model lifecycle management
• Interest in industrial AI and asset-intensive environments
• Industry exposure in Mining, Oil & Gas, Aerospace, Supply Chain, Logistics, or Renewable Energy

More skills:

Large Language Models (LLMs), LLM/SLM fine-tuning, Retrieval-Augmented Generation (RAG), Scalable APIs, Backend systems, Cloud platforms (AWS, GCP, Azure), System design, Debugging, Performance optimization, Production reliability

Other:

• Work in a high-growth startup environment with agility, collaboration, and rapid professional growth
• Opportunity to work on AI-driven projects that drive real change across industries and improve lives
• Platform built on Computational Knowledge Graph foundation integrating diverse data types for AI applications