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GetSetYo Technology Labs Private Limited
GetSetYo Technology Labs Private Limited cover picture
GetSetYo Technology Labs Private Limited logo

GetSetYo Technology Labs Private Limited

https://getsetyo.com
Founded :
2022
Type :
Products & Services
Size :
0-20
Stage :
Raised funding

About

Plan your dream vacation effortlessly with Getsetyo! Benefit from personalized guidance by our travel experts and enjoy exclusive deals. Trusted by over 1000 customers and highly rated at 4.9/5 stars on Google. Great deals on packages for Bali, Thailand, Dubai, Singapore, Europe, Vietnam, Malaysia, Japan, Spain, Italy, Geogria and more
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Jobs at GetSetYo Technology Labs Private Limited

GetSetYo Technology Labs Private Limited
at GetSetYo Technology Labs Private Limited
Abhishek Agrawal
Posted by Abhishek Agrawal
Bengaluru (Bangalore)
1 - 10 yrs
₹20L - ₹60L / yr (ESOP available)
Generative AI
Agentic AI

AI Engineer (1–8 years)

Location: Bellandur, Outer Ring Road, Bengaluru (Work from Office only)

Organization Size: 20 members across functions, and growing

Reports to: Head of Engineering

GetSetYo is a Bangalore-based early-stage travel tech startup, built by internet industry veterans (ex-Makemytrip, Flipkart, Ola, PhonePe, Zynga, MagicPin, etc.) and premier academic institutes (IIT Delhi, IIT BHU, ISB, DCE, NIT Surathkal, etc.), and funded by multiple unicorn founders (of companies such as MakeMyTrip, Zomato, Groww, Udaan, MaMaEarth, etc.) and we’re growing fast. Look us up here - https://www.getsetyo.com/about

We’re building something exciting in the travel tech space and looking for a senior AI Engineer to join our core engineering team in Bangalore.

Who Are You:

  • 1–8 years of total software engineering experience, including at least 1 year building and shipping AI/ML or LLM-powered products in production
  • Engineering degree from a top-ranked college
  • Strong engineering foundation in Python or Java, with the ability to build reliable backend services, APIs, evaluation pipelines, and developer tooling around AI systems
  • Hands-on experience with LLM application patterns such as RAG, tool/function calling, structured output generation, vector search, reranking, and agentic workflows
  • Familiarity with agent frameworks and orchestration patterns, including multi-step workflows, planner/executor patterns, tool routing, and guardrails
  • Working knowledge of MCP (Model Context Protocol) or similar patterns for connecting models to internal tools, data sources, and external systems
  • Strong understanding of context engineering, prompt design, and how to manage instructions, conversation state, tools, memory, and retrieved context for consistent model behavior
  • Experience with evaluation and observability for AI systems: offline evals, online metrics, regression testing, trace inspection, cost/latency monitoring, and failure analysis
  • Comfortable working in a fast-paced startup where you can own problem statements end to end — from prototype to production rollout
  • Must have experience using AI-native developer tools such as Claude Code / coding agents / AI-assisted workflows to accelerate delivery

What You’ll Do

  • Build and own production-grade AI features across the stack, from experimentation and prototyping to backend integration, deployment, monitoring, and iterative improvement
  • Design and implement agentic workflows for real user problems — combining LLM reasoning, retrieval, tool use, business rules, and backend APIs into reliable multi-step systems
  • Build and optimize RAG and search systems: document ingestion, chunking strategies, embedding pipelines, vector indexes, hybrid retrieval, reranking, and citation/grounding flows
  • Integrate models with internal and external systems through tool calling, APIs, and where relevant MCP-compatible interfaces, so models can safely access the right context and take useful actions
  • Drive context engineering for AI products: decide what memory, instructions, retrieved context, tool outputs, and interaction history should be passed to the model at each step for maximum quality and efficiency 
  • Build evaluation systems for prompts, agents, and retrieval quality — including benchmark datasets, golden test cases, automated regression checks, and human-in-the-loop review workflows
  • Establish observability and debugging for AI pipelines: traces, tool execution logs, latency/cost tracking, hallucination analysis, and failure-mode investigation 
  • Help define engineering standards for AI systems across security, guardrails, versioning, rollback, experimentation, and cost-performance tradeoffs

What We Offer:

  • AI Impact from Day 1: Lead the development of our core ML capabilities
  • Fast Iteration: Weekly releases and direct user feedback
  • Collaborative Culture: Flat structure and transparent communication
  • Vibrant Office: In-person energy in Bangalore HQ
  • Perks: Employee travel discounts and exclusive deals.


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