- Strong software engineering background and experience in production-grade AI delivery systems
- Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
- Proficient in at least one skill in C++/CUDA, Python/PySpark, Java/Scala
- Good experience in cloud-native AI tools (Azure, AWS, GCP), Agentic/DL/LLM/ML frameworks (React, LangChain, LangGraph, TensorFlow, PyTorch, OpenCV, Hugging Face), and AIOps platforms
- Robust in GPU based accelerating computing technologies (CUDA, Rapids, NeMo, NIM, etc.)
- Strong in Graph Theory or Knowledge Graph related architecture and database (e.g. Neo4j, cuGraph)
- Proficiency in model evaluation, distributed training, and hyperparameter optimization
- Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis,



Elastic Search)
- Proficiency in model evaluation, distributed training, and hyperparameter optimization
- Get familiar with AI/ML lifecycle, model architectures (including deep reinforcement learning, LLMs, RAG, vector search, MoE, foundation models), and structured/unstructured data pipelines
- Effective communicator who can explain complex technical ideas to technical and business audiences
- Ability to work independently in fast-paced, cross-functional environments
- Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, or a related technical field; PhD preferred
- Academic or applied focus on AI, deep learning, or intelligent systems is preferred

📌 DE&A - AIML - Auto ML (Pune)
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