Machine Learning Engineer

About us:

ConveGenius is an AI-native education infrastructure company transforming how governments, enterprises, and social institutions deliver learning and governance outcomes at population scale. Operating across B2G and B2B segments, the company partners with state governments, CSR funders, philanthropies, and social enterprises to build intelligent education ecosystems that are both impactful and commercially sustainable.

At its core, ConveGenius runs two synergistic business lines:

• SwiftAI – the intelligence and automation layer that powers citizen engagement, governance efficiency, and real-time decision-making for governments and institutions.
• SwiftSchool – the learning infrastructure layer that integrates adaptive learning, digital classrooms, and analytics to drive measurable student and system outcomes.

This dual-engine model enables ConveGenius to serve as the technology backbone for education reform — connecting classrooms, communities, and command centres through a unified AI-powered framework. Unlike traditional edtech companies that deliver content, ConveGenius builds infrastructure — platforms that integrate deeply into public delivery systems while enabling private and philanthropic partners to scale their missions with measurable results.

With operations across 22+ Indian states and partnerships with multilaterals, CSR alliances, and government departments, ConveGenius has established itself as a partner-friendly enterprise trusted for aligning policy priorities with social and financial returns. Supported by institutional investors and guided by a double bottom line philosophy, the company consistently delivers impact at scale and profitability with purpose.

As ConveGenius enters its next phase of growth, it is shaping the future of AI-led public education infrastructure — where technology, partnerships, and governance intelligence converge to redefine how nations learn and thrive.

Learn more about us: https://linktr.ee/convegenius11

As the Sr. 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.

This role is part of a 0-to-1 vertical build. The person joining should be comfortable with ambiguity, high ownership, rapid iteration, hands-on execution, team building, and periods of high-intensity work during the early setup phase.

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 environment.
• Working knowledge of 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