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Company: Antal International Website: Visit Website Business Type: Startup Company Type: Product Business Model: B2B Funding Stage: Pre-seed Industry: Information Technology This is a Permanent role with a valued client of Antal International Were looking for a passionate AI researcher to advance agentic reasoning and decision intelligence systems. This role bridges cutting-edge research and real-world AI applications , ideal for someone who thrives on open-ended problems and scientific innovation . What Youll Do - Conduct core scientific research to improve agentic reasoning reliability and learning. - Define agentsettingreward formulations for decision intelligence workflows. - Frame learning problems using Deep Reinforcement Learning, preference learning, or supervised fine-tuning. - Design and evaluate reasoning paradigms such as Chain-of-Thought, Tree-of Thought, and multi-step planning. - Curate datasets for training and evaluating reasoning agents. - Contribute to knowledge system learning, including graph updates and ontology refinement. - Collaborate closely with AI Engineers to translate research outcomes into production systems. What Were Looking For Education - Bachelors or Masters in Computer Science, AI, Data Science, or a related field. - Advanced degrees or a strong academic research background are preferred. Professional Experience - 26+ years in data science, applied research, or ML engineering roles. - Proven hands-on experience with deep learning frameworks and reinforcement learning projects. Technical Skills - Solid proficiency in PyTorch and TensorFlow. - Working knowledge of reinforcement learning algorithms such as MDPs, PPO, DPO, GRPO, etc. - Experience with transformers and LLM fine-tuning (SFT, LoRA, QLoRA). - Solid understanding of classical machine learning and statistical learning concepts. - Excellent analytical and experimental design skills, with the ability to evaluate AI agents rigorously. Personal Attributes - Strong scientific curiosity and a passion for exploring challenging problems. - Ability to balance deep research with practical impact, delivering meaningful results. - Comfortable working on open-ended, complex problems in a collaborative environment. .