Senior Machine Learning Engineer

PhoenixAI; Mumbai; Hybrid; INR 2,500,000 - 4,500,000; Full time

Job summary

PhoenixAI in Mumbai, India is seeking an experienced ML infrastructure engineer to fine-tune, deploy, and scale LLMs and foundation models for design tasks. You will build scalable inference systems and RAG pipelines, aiming for sub-second latency in a cloud-native setting.

Strong Python and PyTorch expertise, experience with LangChain/LlamaIndex, vector search, and high-availability architectures are essential.

Qualifications

• Experience fine-tuning and deploying LLMs for real tasks.
• Strong Python and PyTorch with ML tooling knowledge.
• Familiar with LangChain/LlamaIndex and vector search tech.
• Understanding of scalable, reliable inference systems.

Responsibilities

• Fine-tune, evaluate, and deploy LLMs and foundation models for design tasks.
• Build scalable inference systems, agentic workflows, and RAG pipelines.
• Optimize latency via batching, caching, quantization and streaming.
• Collaborate with AI researchers to operationalize techniques.
• Contribute to cloud-native ML infrastructure decisions.

Skills

LLMs

Prompt tuning

Fine-tuning

Evaluation

Python

PyTorch

LangChain

LlamaIndex

Vector search

Semantic embeddings

RAG systems

High-availability

Cloud-native infra

Job description

AI is reshaping every industry—and Physical AI is next. At PhoenixAI, you'll work at the intersection of cutting-edge ML and Robotics innovation, helping bring frontier models to production. From deploying optimized foundation models to building low-latency inference infrastructure, your work will define how engineers interact with intelligence. If you're excited to make research real and shape the future of design, this role is for you.

What you'll do

• Fine-tune, evaluate, and deploy LLMs and foundation models for specialized chip engineering tasks.
• Build scalable inference systems, agentic workflows, and RAG pipelines to support intelligent, interactive design tools.
• Optimize model performance through batching, caching, quantization, and streaming to achieve sub-second latency.
• Collaborate closely with AI scientists to operationalize experimental techniques and deliver robust, production-grade systems.
• Contribute to architecture decisions around scalable, reliable ML infrastructure in a cloud-native environment.

What You Bring

• Hands-on experience working with LLMs, including prompt tuning, fine-tuning, and evaluation.
• Deep expertise in Python, PyTorch, and the surrounding ML tooling ecosystem.
• Familiarity with orchestration frameworks and agentic workflows (e.g., LangChain, LlamaIndex).
• Strong understanding of vector search, semantic embeddings, and RAG systems.
• Solid grasp of scalable ML architecture and the principles behind high-availability inference systems.

Bonus Points

• Experience working directly with ML researchers or in applied research settings.
• Startup experience or contributions to high-velocity, cross-functional teams.
• Open-source contributions to ML tooling or infrastructure projects.
• Experience deploying models in cloud environments (e.g. AWS SageMaker, Bedrock, vLLM, or SGLang)

What it's like here

We're a fast-moving AI startup with a collaborative, high-trust culture. You'll work side-by-side with top engineers and scientists, translating cutting-edge research into real product impact. We value curiosity, speed, and precision—engineering systems that are elegant, pragmatic, and built to last. If you're passionate about bringing frontier Physical AI to the real world, you'll feel right at home.

Logistics

This position is available in Mumbai, India. We believe a mindmelt between engineers and scientists leads to our professional growth and to great products; we have a hybrid schedule.