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