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Mid-level role but niche LLM/PyTorch requirements limit applicant competition.
Specialized ML/LLM engineering skills require domain-specific background, reducing transferability.
Explicit 5–8 years plus mandatory ML/LLM, PyTorch, LangChain, MLOps makes shortlisting stringent.
Own end-to-end machine learning pipelines including model development, hyperparameter tuning, deployment, and MLOps with a focus on transformer-based and LLM models.
Provide expertise in Large Language Models (LLMs) to solve AI problems using state-of-the-art models like OpenAI and apply retrieval augmented generation techniques to enhance model performance.
Collaborate with business and product management to develop and implement analytics solutions and communicate results effectively to technical and non-technical audiences.
5 to 8 years of relevant work experience.
Mandatory skills include Python, Scikit-Learn, PyTorch, SQL, transformer models, LangChain, model building, hyperparameter tuning, model performance metrics, deployment, and deep learning/LLM fine-tuning and evaluation.
Experience with LLM services such as OpenAI models and knowledge of Retrieval augmented generation techniques.
Work Experience Required: 5 to 8 years.
Experienced in building and deploying transformer-based machine learning models and Large Language Models within an end-to-end pipeline.
Skilled at translating structured and unstructured data use cases into scalable AI solutions with a strong analytical and problem-solving approach.
Comfortable working closely with interdisciplinary teams including business and product management to deliver analytics-driven outcomes.