Experience Level: 3–5 Years
Location: Bengaluru, India
About the Role:
We are looking for a driven and experienced Machine Learning Engineer to join our team and help push the boundaries of what’s possible with Large Language Models (LLMs) and intelligent agents. This is a hands-on role for someone with a strong background in LLM tooling, evaluation, and data engineering, and a deep appreciation for building reusable, scalable, and open solutions.
You’ll work across the stack—from agent and tool design, model evaluation, and dataset construction to serving infrastructure and fine-tuning pipelines. We're especially excited about candidates who have made meaningful contributions to open-source LLM/AI infrastructure and want to build foundational systems used by others across the ecosystem.
Key Responsibilities:
- Design, build, and iterate on LLM-powered agents and tools, from prototypes to production.
- Develop robust evaluation frameworks, benchmark suites, and tools to systematically test LLM behaviors.
- Construct custom evaluation datasets, both synthetic and real-world, to validate model outputs at scale.
- Build scalable, production-grade data pipelines using Apache Spark or similar frameworks.
- Work on fine-tuning and training workflows for open-source and proprietary LLMs.
- Integrate and optimize inference using platforms like vLLM, llama.cpp, and related systems.
- Contribute to the development of applications, emphasizing composability, traceability, and modularity.
- Actively participate in and contribute to open-source projects within the LLM/agent ecosystem.
Requirements:
Must-Have Skills:
- 3–5 years of experience in machine learning, with a strong focus on LLMs, agent design, or tool building.
- Demonstrable experience building LLM-based agents, including tool usage, planning, and memory systems.
- Proficiency in designing and implementing evaluation frameworks, metrics, and pipelines.
- Strong data engineering background, with hands-on experience in Apache Spark, Airflow, or similar tools.
- Familiarity with serving and inference systems like vLLM, llama.cpp, or TensorRT-LLM.
- Deep understanding of building componentized ML systems.
Open-Source Contributions:
- Proven track record of contributing to open-source repositories related to LLMs, agent frameworks, evaluation tools, or model training.
- Experience maintaining your own open-source libraries or tooling is a major plus.
- Strong Git/GitHub practices, code documentation, and collaborative PR workflows.
- You’ll be expected to build tools, frameworks, or agents that may be released back to the community when possible.
Nice-to-Have:
- Familiarity with LLM orchestration frameworks like LangChain, CrewAI/AutoGen, Haystack, or DSPy.
- Experience training or fine-tuning models using LoRA, PEFT, or full-scale distributed training.
- Experience deploying LLM applications at scale in cloud or containerized environments (e.g., AWS, Kubernetes, Docker).
What We Offer:
- The opportunity to work on state-of-the-art LLM and agent technologies.
- Encouragement and support for open-source contributions as part of your day-to-day.
- A fast-paced, collaborative, and research-focused environment.
- Influence over architectural decisions in a rapidly evolving space.
To Apply:
Please submit your resume and links to your GitHub, open-source projects, or public technical writing (blog posts, talks, etc.)
