Bachelor's/Master's in Computer Science, AI/ML, Data Science, or related field
Hands-on experience with LLMs (OpenAI, Anthropic, Hugging Face), diffusion models, and transformer architectures
Practical experience evaluating LLM outputs for hallucination, grounding, accuracy, and relevance
Familiarity with LLM fine-tuning techniques (LoRA, PEFT, adapters)
Proficiency in Python, TensorFlow, PyTorch, and ML frameworks
Strong knowledge of NLP, deep learning, and reinforcement learning
Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices
Strong experience with vector databases and Retrieval-Augmented Generation (RAG)
Prior experience in building Gen AI applications for enterprise use cases
Experience working with knowledge graphs and Graph RAG approaches (Neo4j, RDF, or similar)
Excellent problem-solving and communication skills
Strong expertise in prompt engineering including few-shot prompting, chain-of-thought, ReAct, and tool/function calling optimization
Nice to have
Experience with model optimization techniques such as quantization or distillation for performance improvement
Understanding of AI ethics, bias mitigation, and responsible AI principles
Exposure to LLM monitoring and observability (logging, tracing, feedback loops, user evaluation)
Hands-on experience with agentic AI frameworks (planner-executor, tool-using agents, multi-agent systems)
Experience optimizing latency, cost, and token usage in LLM-based systems
Job Requirement
AI/ML
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