Requirements

3–5 years of experience in Machine Learning or Applied AI

Robust ML foundations with hands-on experience in XGBoost, NLP, recommendation systems, personalization, and large-scale retrieval

Deep understanding of LLM architecture, training, and inference

Mandatory experience in LLM post-training (fine-tuning and/or reinforcement learning approaches)

Experience designing model evaluation frameworks and benchmarking methodologies

Strong knowledge of retrieval systems, embeddings, ranking, and memory architectures

Ability to define ambiguous ML problems from first principles and independently drive solutions

Strong experimentation mindset with data-driven decision making

Excellent communication and technical reasoning skills Responsibilities

Own the model selection strategy for production AI systems

Design and build evaluation frameworks for LLM quality and performance





Architect and build Oolka's retrieval and memory layer from scratch

Develop scalable retrieval pipelines and ranking systems

Improve model quality through post-training techniques including fine-tuning and reinforcement learning

Drive research-backed experimentation for continuous model improvement

Work closely with product and engineering teams to translate ambiguous problems into robust ML solutions

Establish best practices for ML experimentation, evaluation, and deployment Job Details

Location: Bengaluru Interview Process

Recruiter Screening

Hiring Manager Discussion

Machine Learning Deep Dive

System Design & Problem Solving

Founder Round

📌 Machine Learning Engineer (Bengaluru)
🏢 ClanX
📍 Bengaluru