Generative AI Lead Engineer

About Us:-

We are one of India’s largest EdTech social enterprises, reaching close to 25

million learners across more than 100,000 schools. Our AI-enabled products make quality

education accessible at scale, spanning personalised adaptive learning, AI teaching assistants,

and intelligent classroom devices, and are designed to operate in low-bandwidth, infrastructure-

constrained environments. As we expand our investment in Generative AI, we are seeking a

senior, hands-on Lead Engineer to design and build our GenAI stack and set the technical

direction for the team.

Role Summary

As the Generative AI Lead Engineer, you will serve as the senior engineer and technical lead

for our GenAI initiatives. You will design the architecture, build the most complex components,

and set the technical direction for the team. The systems you own will range from multi-agent

applications and RAG pipelines to models that run on classroom devices at the edge. This is a

hands-on role focused on building and solving the most challenging technical problems,

translating early-stage product concepts into production GenAI that is scalable and cost-efficient

to operate.

Key Responsibilities

● Technical vision & new development: Own the GenAI roadmap from early prototypes

through to production, and lead decisions on model selection, build versus buy, and

architecture.

● Multi-agent & GenAI systems: Design multi-agent systems (orchestration, tool use,

memory, and agent-to-agent communication via A2A and MCP) and build production-

grade RAG pipelines.

● Edge AI: Lead edge and on-device inference for low-connectivity environments, using

quantization and distillation to run open-weight models locally (e.g., Ollama, vLLM).

● Prompt engineering: Build a systematic, versioned, and tested approach to prompt

development.

● Optimisation: Improve cost, latency, and throughput across our AI systems through

caching, batching, and model routing.

● Deployment & observability: Establish CI/CD for AI systems and instrument tracing

and cost, latency, and quality monitoring (e.g., Langfuse, LangSmith).

● Quality & testing: Build evaluation harnesses and regression suites (LLM-as-judge, A/B

testing) so that every change can be measured.

● Guardrails & Responsible AI: Implement guardrails (validation, safety filters, fallbacks)

and build in bias and hallucination mitigation and data-privacy compliance.

● Application security: Build security into the GenAI stack, including secure coding

practices, defences against prompt injection and data leakage, dependency and

vulnerability scanning, and protection of sensitive user data.

● Technical leadership: Set the technical direction and engineering standards for GenAI,

maintain engineering quality through design and code reviews, and work closely with

Product, Design, and QA to keep delivery on track.

Required Skills & Qualifications

● 8+ years in software or ML engineering, including at least 3 years building GenAI/LLM

systems that have run in production (not just prototypes).

● Experience tech-leading engineering projects from design through to production.

● Deep hands-on experience with LLMs, RAG, agents and tool use, and embeddings,

plus at least one modern agent framework (e.g., Google ADK, LangGraph).

● Strong prompt-engineering skills and an evaluation-driven mindset.

● Strong Python and solid engineering fundamentals (APIs, testing, containers, cloud,

CI/CD).

● Solid experience with databases, including relational and NoSQL stores, and vector

databases for retrieval.

● Familiarity with application security and secure development practices, particularly for

systems handling sensitive user data.

● Real LLMOps/MLOps experience covering deployment, monitoring, and observability in

production.

● A track record of optimising AI systems for cost, latency, and reliability at scale.

Preferred Qualifications

● Edge AI and on-device inference experience (quantization, distillation, constrained

hardware).

● Fine-tuning and PEFT methods such as LoRA.

● Multimodal experience (speech, vision, OCR).

● Full-stack development experience across frontend and backend.

● Experience in EdTech or scalable SaaS platforms.

● Experience deploying and running AI workloads on Google Cloud Platform (GCP).

● Familiarity with responsible-AI and data-privacy frameworks.