AI-Native Full Stack Engineer

Legalease Solutions; Bengaluru; Hybrid; INR 3,500,000 - 7,000,000; Full time

Job summary

Legalease Solutions in Bengaluru, Karnataka, seeks an engineering leader to own end-to-end AI-powered workflows across frontend, backend, and agent logic. You will design and ship multi-step agentic workflows that retrieve documents, use tools, and complete drafting and review tasks that a paralegal would normally perform manually.

You will wire LLMs, RAG pipelines, and document-processing chains against real legal source material, tailoring them to firm practices while implementing guardrails,

Qualifications

• 57 years shipping production software full-stack you've owned something end-to-end that real users depended on.
• Frontend stack (React/Next.js/Vue) and backend stack (Python/Node.js/Java/.NET) – strong in at least one of each.
• You've actually built something with LLMs in production an agent, a RAG pipeline, a document intelligence system and can talk about what broke and how you fixed it, not just what the architecture diagram looked like.
• Solid instincts on system design, security, and performance — you know when a deterministic script beats an agent, and you're not afraid to say so.
• You already use AI coding assistants / agents as part of how you write code day to day.
• APIs, databases, cloud (AWS/GCP/Azure), containers, CI/CD, production monitoring — the boring fundamentals that keep 2am pages from happening.

Responsibilities

• Ship end-to-end AI-powered workflows frontend, backend, agent logic, evals as one person, not as a relay race across three teams.
• Build multi-step agentic workflows that retrieve documents, use tools, and complete drafting, review, and research tasks a paralegal or associate would otherwise do by hand.
• Wire up LLMs, RAG pipelines, and document-processing chains against real legal source material not toy datasets and adapt them to each firm's practice area and working style.
• Put guardrails around everything: evals, monitoring, fallback paths, and a human-review step for anything that touches legal risk.
• Own token economics like a founder owns burn routing, caching, batching, and picking the right model for the right step.
• Use coding agents and AI-assisted dev tools as your default way of writing software we expect you to move faster because of AI.
• Sit close to attorneys and legal engineers so the workflows you build reflect how legal work actually happens at a small firm.

Skills

End-to-end AI workflows

Frontend (React/Next.js/Vue)

Backend (Python/Node.js/Java/.NET)

LLMs in production

System design & security

AI coding assistants / agents

Tools

AWS/GCP/Azure

CI/CD

Monitoring

Job description

About This Role

• Ship end-to-end AI-powered workflows frontend, backend, agent logic, evals as one person, not as a relay race across three teams.
• Build multi-step agentic workflows that retrieve documents, use tools, and complete drafting, review, and research tasks a paralegal or associate would otherwise do by hand.
• Wire up LLMs, RAG pipelines, and document-processing chains against real legal source material not toy datasets and adapt them to each firm's practice area and working style.
• Put guardrails around everything: evals, monitoring, fallback paths, and a human-review step for anything that touches legal risk. No silent failures in front of a client's attorney.
• Own token economics like a founder owns burn routing, caching, batching, and picking the right model for the right step, because solo and small-firm pricing only works if delivery cost stays low.
• Use coding agents and AI-assisted dev tools as your default way of writing software we expect you to move faster because of AI, not despite the extra tooling.
• Sit close to attorneys and legal engineers so the workflows you build reflect how legal work actually happens at a small firm, not how engineers assume it happens.

What gets you shortlisted

• 57 years shipping production software full-stack you've owned something end-to-end that real users depended on, not just tickets in a sprint.
• Comfortable in a modern frontend stack (React / Next.js / Vue) and a backend stack (Python / Node.js / Java / .NET) pick your poison, but be genuinely strong in at least one of each.
• You've actually built something with LLMs in production an agent, a RAG pipeline, a document intelligence system and can talk about what broke and how you fixed it, not just what the architecture diagram looked like.
• Solid instincts on system design, security, and performance — you know when a deterministic script beats an agent, and you're not afraid to say so.
• You already use AI coding assistants / agents as part of how you write code day to day.
• APIs, databases, cloud (AWS/GCP/Azure), containers, CI/CD, production monitoring — the boring fundamentals that keep 2am pages from happening.

Extra credit

• You've optimized inference cost for real — model routing, caching, batching, or built the eval harness that told you it was safe to switch models.
• Fine-tuning, post-training, or continual learning experience.
• Hands-on with LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or you've rolled your own agent framework because the existing ones didn't fit.
• Vector DBs, open-source model deployment, or running models locally/on-prem.
• Legal tech, practice management software, contract review, document intelligence, or small-firm/solo practitioner workflow automation — you already speak some of the domain language.

How we think about this role

We're not looking for someone who waits for a spec. We're looking for someone who can sit with an attorney for twenty minutes, understand the actual pain, and come back with working software — not a Jira epic. You should have a strong, opinionated view on where AI genuinely helps versus where a plain rules engine is just better, cheaper, and more auditable. Say that out loud in the interview; we'll take it as a good sign, not a red flag. Proces