This role is for one of Weekday’s clients Salary range: Rs 9000000 - Rs 10000000 (ie INR 90 - 100 LPA) Min Experience: 5+ years Location: Remote (India) JobType: full-time About the Role Build the core agent behaviour behind a revenue-focused AI agent, from the agent harness and evals to fine-tuning and reinforcement learning, and take hard research problems from hypothesis to production. Requirements • Key Responsibilities • Own and advance the agent harness: the core loop, tools, environment, guardrails, and model behaviour that power the agent. • Improve how the agent selects tools, builds context, coordinates across sub-agents, and handles long, multi-step tasks, through systematic experimentation. • Drive model selection across tasks, balancing answer quality, latency, and cost. • Design and build evaluations for complex revenue tasks such as deal health, risk, and pipeline questions, including benchmarks, regression detection, and automated eval pipelines. • Build eval datasets and grading methods for tasks that have no clean ground truth. • Build scoring and prediction models, such as deal health scores from call transcripts and emails, cross-deal pattern mining, and explainable risk signals. • Lead fine-tuning work as the platform moves from harness-heavy work into deeper model training. • Research and design reinforcement learning (RL) approaches that improve agent behaviour. • Improve context engineering and data quality through entity resolution, information extraction, and knowledge and context graphs. • Own ambiguous research problems end-to-end: form hypotheses, design experiments, build the training, eval, and grading infrastructure, and ship results to production. • Work closely with the US-based Head of AI and the applied AI team in India. • Qualifications • 5+ years of experience in applied AI research or applied science, at a senior level. • MS or PhD in Computer Science, Mathematics, Statistics, or another quantitative discipline. • Hands-on experience building and improving agentic systems and agent harnesses, including tool use, multi-step reasoning, and context management. • Hands-on experience with LLMs or large-scale deep learning systems, including training, fine-tuning, and RLHF. • A clear understanding of why models behave the way they do, not only how to use them. • Experience designing eval datasets or frameworks, especially for tasks without clean ground truth. • Proven ability to scope and drive ambiguous research with minimal guidance. • Publications at top-tier peer-reviewed conferences or journals are preferred. • Comfortable working with a US-based leadership team across time zones. • Benefits & Perks • Competitive salary and equity at Series A, ahead of the next funding round. • A remote role, with the team meeting in person in Bangalore. • Meaningful scope from day one: you will own core agent capabilities, not just support them. • Direct work with the Head of AI and a tight feedback loop with founders and product. • A sharp, low-ego team that moves fast and communicates directly. • A chance to shape an AI-first product at a pivotal stage, as it moves from harness work into fine-tuning and RL. Must-have skills Agentic Systems, Agent Harness Design, model finetuning Good-to-have skills LLM, Applied AI, AI research