ML Engineer (Intelligence) - Bengaluru
Company: CLANX
Location: Bengaluru, Karnātaka, India
Type: Full-time
Posted: 2026-08-03
About this role
Build Oolka's core ML intelligence by leading model selection, evaluation frameworks, and the foundational retrieval/memory layer for AI systems. This is a research-driven ML Engineering role focused on first-principles problem solving.
Company Details
Oolka is an AI-powered consumer fintech company focused on helping users improve and manage their credit health using AI.
Website: [https://oolka.in/](https://oolka.in/)
Requirements
- 3–5 years of experience in Machine Learning or Applied AI
- Strong 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 Des...