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Tier-1 brand, mid-level ML role, and metro location increase applicant competition.
Specialized LLM fine-tuning, RAG and agentic workflows require ML/AI-specific backgrounds.
Mandatory LLM frameworks, vector DBs, and explicit years make shortlisting stringent.
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Design and implement Retrieval-Augmented Generation (RAG) solutions to improve large language model (LLM) capabilities with domain-specific fine-tuning (e.g., Wireless/5G).
Develop and maintain autonomous agentic AI workflows for multi-step processes using advanced AI models and frameworks.
Build and execute robust evaluation frameworks to measure and iterate on AI model performance at enterprise scale with focus on ultra-low latency and high scalability.
Bachelor's degree in Engineering, Computer Science, or related field with minimum 3 years software engineering experience, or Master's with 2+ years, or PhD with 1+ year.
1-8 years of experience specifically in AI/Machine Learning engineering.
Proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.
Experience with vector databases (Milvus, Pinecone), and AI toolkits including LangChain, LlamaIndex, and Transformers.
Work Experience Required: 1-8 years in AI/ML engineering (included here as it is explicit).
Has deep expertise in designing and implementing RAG-based AI solutions and fine-tuning LLMs for domain-specific applications such as Wireless/5G.
Experienced in building scalable, autonomous AI workflows integrating multiple AI/ML components under latency and throughput constraints.
Proficient with Python-based AI ecosystem including PyTorch/TensorFlow, vector DBs, and advanced AI frameworks such as LangChain and LlamaIndex, with familiarity in prompt engineering and reinforcement learning.