LLM or GenAI Application Engineer

FocusKPI is looking for an  LLM or GenAI Application Engineer  to join one of our clients, a high-tech SaaS company.

An LLM or GenAI Application Engineer or LLM Research Engineer role is mainly focused on the consumer-facing applications.

The role will be a part of the core GenAI AI development team within the client.

It primarily contributes to LLM-based application development, evaluation, and testing of new features, as well as core technology, such as an agent framework, utilizing the latest technology. stack, LLM technology.

Work Location :

• Mountain View, CA Duration :  12-month contract; Hybrid role (4 days per week onsite) Pay Range :   $110 / hr to $120 / hr
• No C2C resumes are considered
• Role & Responsibilities : Design, train, and fine-tune large language models (e.g., GPT, LLaMA, PaLM) for various applications.
• Research cutting-edge techniques in natural language processing (NLP) and machine learning to improve model performance.
• Explore advancements in transformer architectures, multi-modal models, and emergent AI behaviors.
• Collect, clean, and preprocess large-scale text datasets from diverse sources.
• Develop and implement data augmentation techniques to improve training data quality.
• Ensure data is free from bias and aligned with ethical AI standards.
• Optimize model architecture to improve accuracy, efficiency, and scalability.
• Implement techniques to reduce latency, memory footprint, and inference time for real-time applications.
• Collaborate with MLOps teams to deploy LLMs into production environments using Docker, Kubernetes, and cloud.
• Develop robust evaluation pipelines to measure model performance using key metrics like accuracy, perplexity, BLEU, and F1 score.
• Continuously test for bias, fairness, and robustness of language models across diverse datasets.
• Conduct A / B testing to evaluate model improvements in real-world applications.
• Stay updated with the latest advancements in generative AI, transformers, and NLP research.

Contribute to research papers, patents, and open-source projects—present findings and insights at conferences and internal knowledge-sharing sessions.  Qualifications :

Top 3 skills (must have) :

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