AI Engineer

at EaseMyTrip.com

AI Engineer

EaseMyTrip.com

Company

Home

AI Engineer

at EaseMyTrip.com

Posted by Zainab Siddiqui

2 - 3 yrs

₹6L - ₹10L / yr

Gurugram

Skills

Large Language Models (LLM)

Langchaing

Huggingface

Amazon Web Services (AWS)

Google Cloud Platform (GCP)

Windows Azure

NOSQL Databases

Python

About the Role

We are looking for a talented LLM & Backend Engineer to join our AI innovation team at EaseMyTrip.com and help power the next generation of intelligent travel experiences. In this role, you will lead the integration and optimization of Large Language Models (LLMs) to create conversational travel agents that can understand, recommend, and assist travelers across platforms. You will work at the intersection of backend systems, AI models, and natural language understanding, bringing smart automation to every travel interaction.

Key Responsibilities:

LLM Integration: Deploy and integrate LLMs (e.g., GPT-4, Claude, Mistral) to process natural language queries and deliver personalized travel recommendations.
Prompt Engineering & RAG: Design optimized prompts and implement Retrieval-Augmented Generation (RAG) workflows to enhance contextual relevance in multi-turn conversations.
Conversational Flow Design: Build and manage robust conversational workflows capable of handling complex travel scenarios such as booking modifications and cancellations.
LLM Performance Optimization: Tune models and workflows to balance performance, scalability, latency, and cost across diverse environments.
Backend Development: Develop scalable, asynchronous backend services using FastAPI or Django, with a focus on secure and efficient API architectures.
Database & ORM Design: Design and manage data using PostgreSQL or MongoDB, and implement ORM solutions like SQLAlchemy for seamless data interaction.
Cloud & Serverless Infrastructure: Deploy solutions on AWS, GCP, or Azure using containerized and serverless tools such as Lambda and Cloud Functions.
Model Fine-Tuning & Evaluation: Fine-tune open-source and proprietary LLMs using techniques like LoRA and PEFT, and evaluate outputs using BLEU, ROUGE, or similar metrics.
NLP Pipeline Implementation: Develop NLP functionalities including named entity recognition, sentiment analysis, and dialogue state tracking.
Cross-Functional Collaboration: Work closely with AI researchers, frontend developers, and product teams to ship impactful features rapidly and iteratively.

Preferred Candidate Profile:

Experience: Minimum 2 years in backend development with at least 1 year of hands-on experience working with LLMs or NLP systems.
Programming Skills: Proficient in Python with practical exposure to asynchronous programming and frameworks like FastAPI or Django.
LLM Ecosystem Expertise: Experience with tools and libraries such as LangChain, LlamaIndex, Hugging Face Transformers, and OpenAI/Anthropic APIs.
Database Knowledge: Strong understanding of relational and NoSQL databases, including schema design and performance optimization.
Model Engineering: Familiarity with prompt design, LLM fine-tuning (LoRA, PEFT), and evaluation metrics for language models.
Cloud Deployment: Comfortable working with cloud platforms (AWS/GCP/Azure) and building serverless or containerized deployments.
NLP Understanding: Solid grasp of NLP concepts including intent detection, dialogue management, and text classification.
Problem-Solving Mindset: Ability to translate business problems into AI-first solutions with a user-centric approach.
Team Collaboration: Strong communication skills and a collaborative spirit to work effectively with multidisciplinary teams.
Curiosity and Drive: Passionate about staying at the forefront of AI and using emerging technologies to build innovative travel experiences.

About the Role

Key Responsibilities:

LLM Integration: Deploy and integrate LLMs (e.g., GPT-4, Claude, Mistral) to process natural language queries and deliver personalized travel recommendations.
Prompt Engineering & RAG: Design optimized prompts and implement Retrieval-Augmented Generation (RAG) workflows to enhance contextual relevance in multi-turn conversations.
Conversational Flow Design: Build and manage robust conversational workflows capable of handling complex travel scenarios such as booking modifications and cancellations.
LLM Performance Optimization: Tune models and workflows to balance performance, scalability, latency, and cost across diverse environments.
Backend Development: Develop scalable, asynchronous backend services using FastAPI or Django, with a focus on secure and efficient API architectures.
Database & ORM Design: Design and manage data using PostgreSQL or MongoDB, and implement ORM solutions like SQLAlchemy for seamless data interaction.
Cloud & Serverless Infrastructure: Deploy solutions on AWS, GCP, or Azure using containerized and serverless tools such as Lambda and Cloud Functions.
Model Fine-Tuning & Evaluation: Fine-tune open-source and proprietary LLMs using techniques like LoRA and PEFT, and evaluate outputs using BLEU, ROUGE, or similar metrics.
NLP Pipeline Implementation: Develop NLP functionalities including named entity recognition, sentiment analysis, and dialogue state tracking.
Cross-Functional Collaboration: Work closely with AI researchers, frontend developers, and product teams to ship impactful features rapidly and iteratively.

Preferred Candidate Profile:

Experience: Minimum 2 years in backend development with at least 1 year of hands-on experience working with LLMs or NLP systems.
Programming Skills: Proficient in Python with practical exposure to asynchronous programming and frameworks like FastAPI or Django.
LLM Ecosystem Expertise: Experience with tools and libraries such as LangChain, LlamaIndex, Hugging Face Transformers, and OpenAI/Anthropic APIs.
Database Knowledge: Strong understanding of relational and NoSQL databases, including schema design and performance optimization.
Model Engineering: Familiarity with prompt design, LLM fine-tuning (LoRA, PEFT), and evaluation metrics for language models.
Cloud Deployment: Comfortable working with cloud platforms (AWS/GCP/Azure) and building serverless or containerized deployments.
NLP Understanding: Solid grasp of NLP concepts including intent detection, dialogue management, and text classification.
Problem-Solving Mindset: Ability to translate business problems into AI-first solutions with a user-centric approach.
Team Collaboration: Strong communication skills and a collaborative spirit to work effectively with multidisciplinary teams.
Curiosity and Drive: Passionate about staying at the forefront of AI and using emerging technologies to build innovative travel experiences.

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About EaseMyTrip.com

Founded :

2008

Type :

Product

Size :

500-1000

Stage :

Profitable

About

EaseMyTrip commenced its operations in 2008 by focusing on the B2B2C (business to business to customer) distribution channel and providing travel agents access to its website to book domestic travel airline tickets in order to cater to the offline travel market in India. Subsequently, by leveraging its B2B2C channel, the company commenced operations in the B2C (business to customer) distribution channel in 2011by primarily focusing on the growing Indian middle class population's travel requirements. With our presence in the B2B2C and B2C channels, we were able to commence operations in the B2E (business to enterprise) distribution channel in 2013 with the aim of providing end-to-end travel solutions to corporates. Our presence in three distinct distribution channels provide us with a diversified customer base and wide distribution network. We believe that the strength of our brand, the quality of our services, our user-friendly websites (www.easemytrip.com and www.easemytrip.in), android and iOS based mobile applications (EaseMyTrip), our customer centric approach, as well as our efficient marketing programs have enabled us to develop significant market share in the domestic airline ticket business in India. In Fiscal 2019, GoAir, and SpiceJet, recognized us as amongst the top travel partners in terms of revenue and passenger count. The strength of our brand has increased significantly over the years. Our technology-enabled infrastructure and systems have enabled us to operate and maintain an efficient and lean organization related to the size of our operations. We have not required any equity infusion subsequent to our original incorporation requirements, and we have historically financed our working capital requirements and the expansion of our business and operations primarily through funds generated from our operations and debt financing.

Prashant Pitti

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