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Job Title: Senior AI/ML Engineer – LLM & Generative AI Experience: 8+ Years Location: Remote Employment Type: Full-Time Job Summary: We are seeking an experienced AI/ML Engineer with a strong focus on Large Language Models (LLMs) and Generative AI . The ideal candidate will have 8+ years of experience in NLP and transformer-based architectures , with a deep understanding of text generation , prompt engineering , and fine-tuning transformer models for enterprise-scale solutions. Key Responsibilities: Design, fine-tune, and deploy state-of-the-art LLMs for a variety of NLP tasks including text generation , summarization , question answering , classification , and translation . Work with transformer-based models such as GPT, BERT, T5, RoBERTa , and others for both research and production purposes. Fine-tune pre-trained models using domain-specific datasets , optimizing for performance and accuracy. Develop and iterate instruction-based prompts , leveraging zero-shot, few-shot, and many-shot learning techniques. Apply prompt engineering best practices to create templates that are adaptable, reusable, and context-aware. Implement chain-of-thought (CoT) prompting strategies to guide complex reasoning tasks. Proactively address challenges such as bias , hallucinations , and knowledge cutoffs that affect model outputs. Collaborate with cross-functional teams to integrate LLM-based features into applications and platforms. Evaluate and improve model performance through A/B testing, prompt iteration, and error analysis. Must-Have Skills: 8+ years of experience in AI/ML , with a strong focus on NLP and transformer-based models . Hands-on expertise with GPT , BERT , T5 , RoBERTa , and related models. Strong understanding of attention mechanisms , context windows , tokenization , embedding layers , and self-attention . Proven experience with fine-tuning and training transformer models. Deep knowledge of prompt engineering , including crafting effective prompts for various task types. Experience using zero-shot , few-shot , and instruction-based prompting approaches. Proficiency in Python and NLP libraries like Hugging Face Transformers , SpaCy , NLTK , etc. Familiarity with model deployment and MLOps workflows in NLP. Preferred Qualifications: Experience with LLM optimization and compression techniques. Understanding of ethical AI , bias mitigation , and LLM limitations . Experience deploying models in cloud environments (AWS, GCP, Azure). Strong communication and collaboration skills to work with product and engineering teams. Background in research or published work in NLP/LLM-related areas is a plus. Show more Show less
