Senior Machine Learning Engineer

Weekday AI (YC W21); Dadri; On-site; INR 5,000,000 - 7,000,000; Full time

Job summary

Weekday AI is hiring an AI infrastructure engineer for Noida to design, build, and deploy production-grade LLM applications with RAG and tool-calling capabilities tailored for education use cases. You will own backend architectures—from data pipelines to orchestration APIs—ensuring performance, scalability, and maintainability in a cloud-native environment.

This role offers high ownership in a 0-to-1 vertical build and the opportunity to contribute to population-scale AI infrastructure impacting

Qualifications

• 5+ years of experience building production-grade AI systems.
• Strong Python software engineering with cloud services experience.
• Experience in RAG/agentic systems in production environments.
• Backend data pipelines and ML model serving expertise.

Responsibilities

• Build Production LLM Systems: design, build and deploy production-grade LLM apps with RAG pipelines and tool-calling.
• End-to-End System Architecture: own backend services, data pipelines, and deployment infra.
• LLM Evaluation & Monitoring: create benchmarks and monitoring for quality before release.
• Latency & Cost Optimization: implement caching, batching, and efficient routing.
• Data & Model Integration: pipelines for multilingual/regional contexts and model integration.

Skills

Python

Machine Learning

RAG Systems

LangChain

REST APIs

Tools

LangChain

LangGraph

Docker

Kubernetes

Job description

Job Description:

This role is for one of Weekday’s clients
Salary range: Rs 5000000 - Rs 7000000 (ie INR 50 - 70 LPA)

Min Experience: 5+ years
Location: Noida, Uttar Pradesh, India
JobType: full-time

We are hiring for a high-impact, AI-native education infrastructure organization that operates across government (B2G) and enterprise (B2B) sectors. Their platforms power intelligent learning systems, adaptive digital classrooms, and AI-driven governance tools reaching over 150 million children across India.

This role is part of a 0-to-1 vertical build, offering a high degree of ownership, rapid iteration, and direct contribution to a mission-driven, population-scale AI infrastructure.

Key Responsibilities

• Build Production LLM Systems: Design, build, and deploy production-grade LLM applications, including Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and tool-calling systems tailored for education use cases (e.g., adaptive feedback, automated Q&A, and curriculum alignment).
• End-to-End System Architecture: Own full backend and AI service architectures—from data pipelines and retrieval layers to orchestration APIs and deployment infrastructure—using clean, maintainable, and tested production code.
• LLM Evaluation & Monitoring: Build rigorous evaluation harnesses, task-specific benchmarks, automated regression tests, and human-in-the-loop validation frameworks to guard quality before release.
• Latency & Cost Optimization: Implement caching, request batching, prompt-context optimization, and model routing to ensure fast, scalable, and cost-effective production deployment.
• Data & Model Integration: Build grounding and instruction data pipelines for multilingual and regional contexts. Evaluate and integrate open-source, closed, and sovereign LLMs into production serving frameworks.

Key Qualifications

• Production Engineering: Strong Python software engineering skills with experience writing production-grade, maintainable code (beyond notebooks and scripts) and owning cloud services end-to-end.
• RAG & Agentic Systems: Hands-on experience building and shipping RAG or agentic systems in production using frameworks like LangChain, LangGraph, or equivalent orchestration platforms.
• Evaluation Frameworks: Proven track record in building automated LLM evaluation systems, benchmark suites, and quality monitoring pipelines.
• Backend & Cloud Infrastructure: Strong experience in backend engineering (REST APIs, data pipelines, containerized deployment, and cloud infrastructure) for serving ML/LLM models at scale.

Good to Have

• Hands-on model fine-tuning experience (LoRA, QLoRA, SFT, DPO).
• Experience with inference optimization (quantization, ONNX, efficient serving) or distributed training frameworks (DeepSpeed, FSDP).

Why Join?

• Work on high-ownership, 0-to-1 AI product development with immense scale and real-world impact.
• Competitive compensation package with comprehensive benefits.
• High-growth, collaborative environment building state-of-the-art public AI systems.

Must-have skills

• Python, Machine Learning

Good-to-have skills

• Artificial Intelligence