Machine Learning Engineer – LLMs & Agent Systems at Affogato AI

Affogato is building a new way for creatives around the world to build better graphics with the power of Gen AI

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.)

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