Description

About RESULTICKS RESULTICKS is a global, award-winning AI-powered Customer Engagement and MarTech platform enabling brands to deliver real-time, personalized, data-driven experiences. With advanced AI, big data cloud solutions, and the world’s first customer data blockchain, RESULTICKS empowers organizations with 360-degree customer insights and omnichannel orchestration. Headquartered in Singapore and New York, RESULTICKS operates globally across the USA, India, Southeast Asia, and beyond, serving leading B2B and B2C brands. The company has been recognized multiple times in Gartner’s Magic Quadrant and awarded by Microsoft for excellence in AI-driven customer experience. Job Summary RESULTICKS is looking for a passionate and skilled Software Engineer – Data Science with 1–4 years of experience to work on Generative AI and Large Language Models (LLMs). The role focuses on researching, developing, fine-tuning, and deploying AI-driven solutions that enable data-driven customer engagement and intelligent decision-making across marketing platforms. Key Responsibilities Generative AI & LLM Development Explore, experiment, and implement Generative AI models such as GANs, VAEs, diffusion models, and Large Language Models (GPT, BERT, LLaMA) Fine-tune pre-trained LLMs for domain-specific and task-specific use cases Apply prompt engineering and prompt optimization techniques NLP & Data Science Perform NLP tasks including text classification, sentiment analysis, and entity recognition Collect, clean, preprocess, and analyze large-scale text datasets Use LLMs to assist in data cleaning, feature extraction, and data understanding Model Building & Evaluation Develop, train, and evaluate machine learning and deep learning models Measure model performance using relevant metrics (perplexity, BLEU score, accuracy, etc.) Perform hyperparameter tuning to improve model efficiency and accuracy Visualization & Communication Create visualizations and analytical reports to communicate insights Present findings to both technical and non-technical stakeholders clearly Collaboration & Deployment Work closely with engineers, product managers, and researchers to deliver AI solutions Assist in deploying LLM-powered applications into production environments Monitor model performance and support retraining and optimization Continuous Learning Stay updated with the latest advancements in Generative AI, LLMs, and AI tooling Required Skills & Qualifications Technical Skills Strong proficiency in Python Hands-on experience with libraries such as NumPy, Pandas, Scikit-learn Experience with TensorFlow / PyTorch Familiarity with Hugging Face Transformers Solid understanding of machine learning, deep learning, and statistics Experience with NLP techniques Knowledge of data visualization tools (Matplotlib, Seaborn, Tableau, Power BI) Working knowledge of Git and cloud platforms (AWS, GCP, Azure – preferred) Preferred Skills Fine-tuning and deploying Large Language Models Experience with RAG (Retrieval-Augmented Generation) architectures Familiarity with LangChain, LangGraph, and vector databases Production deployment of AI/LLM solutions Education Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field Core Competencies Strong analytical and problem-solving skills Excellent verbal and written communication Ability to work collaboratively in cross-functional, global teams

Role and Responsibilities

• Key Responsibilities Generative AI & LLM Development Explore, experiment, and implement Generative AI models such as GANs, VAEs, diffusion models, and Large Language Models (GPT, BERT, LLaMA) Fine-tune pre-trained LLMs for domain-specific and task-specific use cases Apply prompt engineering and prompt optimization techniques NLP & Data Science Perform NLP tasks including text classification, sentiment analysis, and entity recognition Collect, clean, preprocess, and analyze large-scale text datasets Use LLMs to assist in data cleaning, feature extraction, and data understanding Model Building & Evaluation Develop, train, and evaluate machine learning and deep learning models Measure model performance using relevant metrics (perplexity, BLEU score, accuracy, etc.) Perform hyperparameter tuning to improve model efficiency and accuracy Visualization & Communication Create visualizations and analytical reports to communicate insights Present findings to both technical and non-technical stakeholders clearly Collaboration & Deployment Work closely with engineers, product managers, and researchers to deliver AI solutions Assist in deploying LLM-powered applications into production environments Monitor model performance and support retraining and optimization Continuous Learning Stay updated with the latest advancements in Generative AI, LLMs, and AI tooling