Job highlights

- Demonstrated experience building LLM evaluation systems: benchmarks,regression tests,human-eval workflows,and quality monitoring - not just anecdotal "it works.".

- Experience building and operating backend systems / services: APIs,data pipelines,deployment,and monitoring in a real production environment.

- Good to Have

As the ML Engineer at ConveGenius, you will design, build, and ship production LLM-powered systems that power personalised learning in Indian languages and educational contexts. You own applications end-to-end: RAG and agentic pipelines, evaluation harnesses, and the services that serve them in production - with a strong bar for software engineering, not just model or prompt work. Fine-tuning experience is a plus for this role, not a requirement.

Key Responsibilities

- Design, build, and ship production-grade LLM applications: RAG pipelines, agentic workflows, and tool-calling systems for education-specific use cases such as question answering, content generation, adaptive feedback, and curriculum alignment.

- Own full system architecture: data pipelines, retrieval layers, orchestration, APIs, and service infrastructure - written as maintainable, tested production code, not notebooks or scripts.

- Build rigorous LLM evaluation frameworks: task-specific benchmarks, regression testing, human eval loops, and automated quality gates to catch degradation before it ships.

- Own latency and cost at the application layer: caching strategies, request batching, model/route selection, and prompt-context optimisation to keep production systems fast and affordable at scale.

- Build data pipelines for grounding and instruction data, including multilingual and Indic language sources.

- Assess new open-source and closed model releases for domain applicability, cost, and production readiness.





- Define and track system performance metrics, evaluation benchmarks, and reliability targets across all shipped features.

- Work with open-source and sovereign LLMs, integrating them into production-proven serving and orchestration frameworks.

- Where warranted, fine-tune or adapt foundation models (LoRA/QLoRA/SFT) - a strong plus, not a gating requirement for this role.

Must-Have Skills

- Strong production Python engineering: clean, tested, maintainable code - not scripts or notebooks. Comfortable owning services in production.

- Hands-on experience building and shipping RAG or agentic systems in production: retrieval design, orchestration (e.g. LangChain/LangGraph or equivalent), tool calling, and multi-step reasoning pipelines.

- Demonstrated experience building LLM evaluation systems: benchmarks, regression tests, human-eval workflows, and quality monitoring - not just anecdotal "it works."

- Experience building and operating backend systems/services: APIs, data pipelines, deployment, and monitoring in a real production setting.

- Working knowledge of AI/ML pipelines, data preparation, and model deployment.

- Experience working with open-source and sovereign LLMs, using standard, production-proven frameworks.

- Understanding of cloud platforms and infrastructure for serving ML/LLM systems at scale.

Good to Have

- Hands-on fine-tuning experience with LoRA, QLoRA, SFT, DPO, or similar preference-optimisation techniques.

- Familiarity with DeepSpeed, FSDP, or large-scale distributed training frameworks.

- Experience with inference optimisation (quantisation, ONNX, efficient serving) at the model layer

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 ML Engineer (Noida)
🏢 Convegenius Edu
📍 Noida