Senior Machine Learning Engineer

Company: Vidpro

Website: Visit Website

LinkedIn: Visit LinkedIn

Business Type: Startup

Company Type: Product

Business Model: B2B2C

Funding Stage: Series B

Industry: Information Technology

Salary Range: ₹ 50-70 Lacs PA

Job Description Senior ML Engineer – Generative AI 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.