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AI Agent Developer

AI that moves, thinks, and holds its shape.

AI agent development for startups and software teams that need tool use, orchestration, guardrails, and automation that works beyond the prototype.

AI agents, LLM systems, RAG, agentic workflows, MCP, vector databases, and model infrastructure designed for product reality.

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AI Engineer

Best fit

Best for teams moving from AI prototype to production workflow with real tools, validation, and automation pressure.

Based in

Kathmandu, Nepal

What Changes

Agents that can do real work
Better guardrails
Clearer production behavior

Service Details

What this work looks like in practice.

Most teams searching for an AI agent developer are not buying a demo. They are trying to turn a promising AI workflow into software that can do real work without becoming a support nightmare.

That is where I fit best.

I build AI agent systems for startups and software teams that need workflows, tool use, and product constraints handled properly. That includes AI agents, LLM applications, fine-tuning, RAG, agentic RAG, MCP integrations, automation, self-hosted models, open source models, video models, and vector database-backed systems. The model matters, but the real value comes from the surrounding system: the rules, the recovery paths, the validation, and the product logic that keep the agent useful once real users arrive.

What I help with

  • AI agents that call tools and complete repeatable workflows
  • Internal automation that replaces manual operational work
  • Product features that need reasoning plus real actions
  • Multi-step AI workflows for support, research, coding, or ops
  • LLM systems that need validation, logging, and guardrails
  • Self-hosted model deployments, open source model evaluation, and vector database design
  • MCP-powered workflows and model infrastructure that connect AI to real product systems

Why teams hire me for this

Because prompt quality is the easy part. Production AI agents also need:

  • clear tool contracts
  • output validation where mistakes matter
  • state management that stays readable
  • observability for debugging and improvement
  • cost and latency discipline
  • clean boundaries between AI behavior and product logic

If you want an AI feature that behaves like product infrastructure instead of a fragile experiment, this is the lane.

Typical fit

This is usually a good fit if your team is trying to:

  • move from prototype to production
  • automate repetitive internal or customer-facing processes
  • integrate LLM systems into an existing product
  • ship AI features without making the architecture brittle

If you need an AI agent developer who can think through product, systems, and delivery, send me the workflow that is stuck.

FAQ

Questions teams usually ask.

What kind of AI agent work do you do? +

The work includes agent workflow design, tool integration, orchestration, memory boundaries, automation, and production hardening rather than one-off prompt experiments.

Do you build AI features or complete agent systems? +

Both. Some teams need one AI-powered workflow inside a product. Others need a broader agent system with tools, validation, monitoring, and recovery loops.

Next Step

Need this kind of engineering support?

Send the product stage, the current bottleneck, and what you need shipped. I will tell you quickly whether this is the right fit.

Email Aayush