We are looking for Ph.d professionals (only from Supply Chain, Logistics, Energy, Oil & Gas, Mining, Aerospace, and Industrial Manufacturing domains).

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 productionbuilding robust AI platforms, optimizing LLM workflows, and delivering high-impact solutions across forecasting, route optimization, anomaly detection, predictive maintenance, and intelligent automation.

With 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 :

- Bachelors or Masters 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.

- 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 stakeholders.

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.

- 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.