360-365 Marketing OPCOptimize and fine-tune large language models (LLMs) for summarization, labeling, question answering, and agentic AI capabilities.
Train and enhance AI models to extract relevant insights from multimodal data sources (voice, text, chat, and SMS).
Develop and implement RAG (Retrieval-Augmented Generation) solutions to improve model performance and response accuracy.
Design and refine prompt engineering strategies for various NLP tasks to ensure accurate and contextually aware outputs.
Implement A/B testing methodologies to evaluate different model versions and improve AI performance.
Develop and maintain scalable ML pipelines for model training, evaluation, and deployment.
Collaborate with data engineers, software developers, and product teams to integrate AI solutions into our platform.
Has experience with CRM (Zoho)
Must-Have Skills
Proven experience in LLM fine-tuning, optimization, and deployment
Deep understanding of Retrieval-Augmented Generation (RAG) solutions
Expertise in prompt engineering for AI-driven applications
Strong background in machine learning, deep learning, and NLP
Proficiency in Python, with experience in key ML libraries:
NumPy, Pandas, SciPy, scikit-learn
Experience with A/B testing and model evaluation techniques
Familiarity with data versioning and MLOps tools like DVC
Proficiency in SQL and working with large datasets
Strong problem-solving skills and the ability to work autonomously in a remote setting
Nice-to-Have Skills:
Experience with vector databases (e.g., FAISS, Pinecone, Weaviate) for RAG applications
Background in agentic AI development for conversational agents
Familiarity with cloud platforms like AWS, GCP, or Azure for model deployment
Knowledge of C++ or R for performance optimization
Work Onsite (BGC, Tagiug City)
15 VL (Convertible to Cash)
Full-time
Engineering, Information Technology
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