Required Expertise
6–12+ years in Data Science / ML Engineering, with deep experience in LLM‑based systems.
Proven experience building agentic architectures (planner‑executor, tool‑use agents, ReAct‑style reasoning).
Strong background in RAG, embeddings, retrieval optimization, and evaluation.
Expertise in NLP, transformers, deep learning, and model fine‑tuning.
Proficiency with PyTorch, HuggingFace, LangChain/LlamaIndex, Ray, Kubernetes, and vector databases.
Experience designing production‑grade ML systems with monitoring, evaluation, and observability.
Strong communication skills and ability to lead technical direction.
Preferred Qualifications
Experience in enterprise search, knowledge management, or high‑compliance domains.
Experience with model distillation, LoRA/QLoRA, PEFT, and model compression.
Experience building evaluation frameworks for hallucination, grounding, and agent reliability.
Familiarity with knowledge graphs, symbolic reasoning, or hybrid neuro‑symbolic systems.
Publications, patents, or open‑source contributions in LLMs or agent systems.
Strong coding skills in Python 7+ years
Be a natural problem solver, able to take a lead in collaborating to resolve issues
Proficiency in IDE debugging : VSCODE and PYCHARM
Have communication skills
5+ years of experience in AI and machine learning
Deep understanding of machine learning algorithms, classification models, diagnostic testing of models
Experience working directly and Transformer based architectures including BERT, RoBERTa, T5 etc. Nd familiarity with large language models and fine tuning
Experience with conversational search / semantic search, reinforcement learning, prompt engineering, hallucination mitigation
Working understanding of the business risks associated with applying LLM (LangChain) in a business
Experience working with AWS, RAG, SageMaker, SQL
Never miss an opportunity! Create an alert based on the job you applied for.
