GenAI / AI-ML Engineer

Two95 International; Gurugram District; Hybrid; INR 4,000,000 - 6,500,000; Full time

Job summary

Two95 International in Gurugram NCR seeks a GenAI/AI-ML Engineer to design, develop, and deploy scalable AI-powered applications. You will build production-ready RAG pipelines and LLM-based systems, with strong Python, ML/DL, and cloud skills.

The role requires building REST APIs with FastAPI, vector databases, and collaboration across Data Engineering, DevOps, and Product teams to deliver reliable, secure AI solutions in production.

Qualifications

• Proficient in Python and SQL for data-centric AI applications.
• Experience building production-grade REST APIs (FastAPI preferred).
• Strong software engineering practices and testing.
• Experience in Agile development environments.

Responsibilities

• Design, develop, test, and deploy scalable AI and GenAI solutions.
• Build RAG pipelines with embedding models and vector databases.
• Develop LLM-powered apps using prompt engineering and agents.
• Fine-tune, evaluate, deploy, and monitor ML/DL models.
• Create REST APIs and backend services for AI apps using FastAPI.
• Design data preprocessing, feature engineering, and model eval pipelines.
• Integrate structured and unstructured data for context-aware AI.
• Implement semantic search and document retrieval architectures.
• Collaborate with Data Eng, DevOps, Product and cross-functional teams.
• Ensure production scalability, reliability, security and performance.

Skills

Python

SQL

REST API

FastAPI

OOP

Agile

Education

Bachelor's degree in CS/Engineering

Tools

Docker

Git

JIRA

CI/CD

Job description

Role Overview

We are looking for an experienced GenAI / AI-ML Engineer with strong hands-on expertise in Python, machine learning, deep learning, Large Language Models, Retrieval-Augmented Generation, and agentic AI systems. The selected candidate will be responsible for designing, developing, and deploying scalable AI-powered applications. The role requires practical experience in building production-ready RAG pipelines, LLM-powered applications, REST APIs, machine-learning models, and cloud-based AI solutions using AWS.

Key Responsibilities

• Design, develop, test, and deploy scalable AI, machine-learning, deep-learning, and Generative AI solutions.
• Build and optimise Retrieval-Augmented Generation pipelines using modern frameworks, embedding models, and vector databases.
• Develop LLM-powered applications using prompt engineering, AI agents, LangGraph, and multi-agent workflows.
• Fine-tune, evaluate, deploy, and monitor machine-learning and deep-learning models.
• Build REST APIs and backend services for AI applications using FastAPI or similar frameworks.
• Design data-preprocessing, feature-engineering, model-training, and model-evaluation pipelines.
• Integrate structured and unstructured data sources to deliver accurate and context-aware AI solutions.
• Implement semantic search and document-retrieval architectures.
• Evaluate RAG and Generative AI solutions using appropriate quality and performance metrics.
• Collaborate with Data Engineering, DevOps, Product, and other cross-functional teams.
• Ensure the scalability, reliability, security, and performance of AI applications in production environments.
• Follow software-engineering best practices, coding standards, version-control processes, and Agile methodologies.
• Troubleshoot model, API, data-pipeline, and production-performance issues.

Mandatory Skills Programming and Backend Development

• Strong hands-on experience in Python.
• Strong working knowledge of SQL.
• Experience developing REST APIs using FastAPI or similar Python frameworks.
• Good understanding of object-oriented programming, modular development, testing, and software-engineering best practices.
• Experience working in Agile development environments.

Machine Learning and Deep Learning

• Hands-on experience with: Scikit-learn TensorFlow PyTorch Keras
• Strong understanding of: Regression Classification Clustering Feature engineering Data preprocessing Model evaluation Hyperparameter tuning Model deployment and monitoring

NLP and Generative AI

• Minimum one year of hands-on experience working on GenAI or LLM-based projects.
• Strong understanding of Large Language Models and Natural Language Processing concepts.
• Experience in prompt engineering and prompt optimisation.
• Hands-on experience designing and implementing RAG architectures.
• Experience building agentic AI or multi-agent applications.
• Practical experience with: LangChain LangGraph OpenAI APIs Hugging Face LangSmith

RAG and Vector Databases

• Experience working with vector databases and similarity-search technologies, including: Pinecone FAISS
• Knowledge of embedding models, chunking strategies, semantic search, document retrieval, and reranking.
• Experience evaluating RAG solutions using metrics or frameworks such as: RAGAS BLEU ROUGE

AWS and DevOps

• Hands-on experience with AWS services such as: Amazon EC2 Amazon S3 Amazon SageMaker Amazon Bedrock

Experience working with

• Docker
• Git
• JIRA
• CI/CD pipelines

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
• Experience deploying AI and LLM applications in production environments.
• Understanding of LLM observability, hallucination control, guardrails, latency optimisation, and cost optimisation.
• Experience integrating enterprise data sources with GenAI applications.
• Strong analytical, problem-solving, communication, and stakeholder-management skills.

Candidate Eligibility

• Candidates must have 4-6 years of overall professional experience.
• At least one year of practical GenAI or LLM project experience is mandatory.
• Candidates must be immediate joiners.
• Candidates should be based in Noida, Gurugram, Delhi, or another NCR location.
• Candidates must be comfortable working in a hybrid model from the Gurugram office.
• Candidates should be available for an interview on August 1 or .

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.