AI Native Engineer - Python/RAG
Summary
Senior (6+ yrs) AI engineer on-site in Gurugram building AI-first travel solutions for TBO's platform: architecting LLM/RAG pipelines, agentic workflows, and low-latency Python microservices with vector databases, cloud infra (AWS/GCP/Azure, Kubernetes), and SQL/NoSQL data stores.
Role Overview:
Looking for a seasoned AI Native Engineer with over 6 years of core software engineering and advanced AI/ML experience to drive next-generation, AI-first travel solutions at our Gurugram office.
In this role, you will be responsible for architecting, building, and deploying scalable AI-driven systems. You will bridge the gap between cutting-edge LLMs, Generative AI frameworks, vector databases, and high-throughput backend infrastructure to transform the travel tech ecosystem.
Key Responsibilities:
- AI Architecture & Implementation: Architect and implement end-to-end AI-native applications using modern LLM frameworks (LangChain, LlamaIndex, AutoGen, or similar).
- System Integration & Scaling: Design low-latency, scalable microservices and APIs to integrate generative AI, agentic workflows, and semantic search directly into TBO's core travel platform.
- Retrieval-Augmented Generation (RAG): Build, optimize, and maintain RAG pipelines utilizing vector databases (Chroma, Pinecone, Weaviate, Qdrant, or Milvus) for accurate data retrieval and knowledge synthesis.
- Agentic Workflows: Design multi-agent workflows and autonomous AI systems capable of executing complex multi-step travel booking, customer query resolution, and internal automation tasks.
- Model Fine-Tuning & Evaluation: Evaluate, fine-tune, and optimize open-source (e.g., Llama, Mistral) and proprietary (e.g., OpenAI, Anthropic, Gemini) LLMs for domain-specific travel tasks.
- Engineering Excellence: Maintain clean, testable, and maintainable code bases adhering to production-ready enterprise standards.
Candidate Qualifications & Requirements:
- Total Experience: 6+ years of relevant experience in software engineering, distributed systems, and AI implementation.
- Education: B.Tech / M.Tech / M.S. in Computer Science or related quantitative field from Tier-1 Institutions (IITs, NIT, BITS Pilani, IIITs, or top global universities).
Tech Stack:
- Languages: Python (Advanced), Go, or Java / C++.
- AI/LLM Frameworks: LangChain, LlamaIndex, AutoGen, Haystack, PyTorch / TensorFlow.
- Vector DBs: Pinecone, Qdrant, Milvus, Weaviate, Chroma, or PGVector.
- Cloud & Infrastructure: AWS / GCP / Azure, Kubernetes, Docker, CI/CD pipelines.
- Databases: PostgreSQL, Redis, MongoDB, Elasticsearch.
- Domain Knowledge: Prior experience in high-scale B2B/B2C SaaS, travel, e-commerce, or transactional platforms is a plus.
Skills
- Agentic AI
- AI
- Anthropic
- API
- AutoGen
- Automation
- AWS
- Azure
- CI/CD
- Cloud
- C++
- Distributed Systems
- Docker
- E-commerce
- Elasticsearch
- Fine Tuning
- GCP
- Generative AI
- Java
- Kubernetes
- LangChain
- LlamaIndex
- LLM
- Machine Learning
- Microservices
- Milvus
- Mistral
- MongoDB
- OpenAI
- pgvector
- Pinecone
- PostgreSQL
- Python
- PyTorch
- Qdrant
- RAG
- Redis
- SaaS
- Semantic Search
- TensorFlow
- Vector Databases
- Weaviate
