AI Engineer III

Jobtailor; Gurugram District; On-site; INR 400,000 - 700,000; Full time

Job summary

Jobtailor is seeking an experienced software/AI engineer to develop and implement AI and Agentic AI solutions across enterprise CRM platforms. You will build intelligent workflows with LLMs, integrate tools, and create production-ready GenAI applications using RAG and prompt engineering.

You'll maintain scalable data pipelines on GCP (BigQuery, Airflow/Composer, DataProc) and collaborate with cross-functional teams to translate business needs into AI-enabled solutions while upholding security

Qualifications

• Bachelor’s degree in technology related discipline or equivalent experience preferred.
• 6-8 years of experience in software engineering, machine learning engineering, data engineering, or applied AI.
• Hands-on experience building or integrating GenAI applications using Large Language Models (LLMs).
• Experience with Agentic AI concepts, including orchestration frameworks, tool calling, context management, or multi-step AI workflows.
• Understanding of Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, and LLM evaluation techniques.
• Strong programming skills in Python and experience developing backend services, APIs, and data pipelines.
• Experience with AI/ML frameworks such as LangChain, LangGraph, PyTorch, TensorFlow, or Scikit-learn
• Working knowledge of Google Cloud Platform (GCP), including BigQuery, Composer (Airflow), DataProc, Cloud Storage, and related data services
• Understanding of cloud-native application development, distributed systems, and modern software engineering practices
• Knowledge of CI/CD, containerization (Docker), and production deployment concepts
• Good problem-solving, analytical, and communication skills with the ability to work collaboratively in cross-functional teams
• Successful completion of a background verification check, subject to applicable laws and regulations

Responsibilities

• Develop and implement AI and Agentic AI solutions that enhance customer engagement, sales effectiveness, and operational efficiency across enterprise CRM platforms
• Build intelligent workflows using Large Language Models (LLMs), enterprise data, and tool integrations to automate customer and business processes
• Develop production-ready GenAI applications, including Retrieval-Augmented Generation (RAG), prompt engineering, and AI-powered business workflows
• Build and maintain scalable data pipelines and AI solutions using Google Cloud Platform (GCP) services such as BigQuery, Composer (Airflow), DataProc, and related cloud services
• Integrate AI applications with enterprise systems, APIs, and data platforms while ensuring operational reliability and performance
• Collaborate with product managers, software engineers, data engineers, and business stakeholders to translate business requirements into AI-enabled solutions
• Contribute to shared AI frameworks, reusable components, evaluation pipelines, and engineering standards
• Ensure AI applications comply with enterprise security, governance, responsible AI, and operational standards

Skills

Python programming

AI/ML frameworks

CI/CD

Docker

Communication

Problem solving

Education

Bachelor's degree in tech related field

Tools

LangChain

LangGraph

PyTorch

TensorFlow

Scikit-learn

BigQuery

Airflow

DataProc

Job description

• Develop and implement AI and Agentic AI solutions that enhance customer engagement, sales effectiveness, and operational efficiency across enterprise CRM platforms
• Build intelligent workflows using Large Language Models (LLMs), enterprise data, and tool integrations to automate customer and business processes
• Develop production-ready GenAI applications, including Retrieval-Augmented Generation (RAG), prompt engineering, and AI-powered business workflows
• Build and maintain scalable data pipelines and AI solutions using Google Cloud Platform (GCP) services such as BigQuery, Composer (Airflow), DataProc, and related cloud services
• Integrate AI applications with enterprise systems, APIs, and data platforms while ensuring operational reliability and performance
• Collaborate with product managers, software engineers, data engineers, and business stakeholders to translate business requirements into AI-enabled solutions
• Contribute to shared AI frameworks, reusable components, evaluation pipelines, and engineering standards
• Ensure AI applications comply with enterprise security, governance, responsible AI, and operational standards

Requirements

• Bachelor’s degree in technology related discipline or equivalent experience preferred
• 6-8 years of experience in software engineering, machine learning engineering, data engineering, or applied AI
• Hands-on experience building or integrating GenAI applications using Large Language Models (LLMs)
• Experience with Agentic AI concepts, including orchestration frameworks, tool calling, context management, or multi-step AI workflows
• Understanding of Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, and LLM evaluation techniques
• Strong programming skills in Python and experience developing backend services, APIs, and data pipelines
• Experience with AI/ML frameworks such as LangChain, LangGraph, PyTorch, TensorFlow, or Scikit-learn
• Working knowledge of Google Cloud Platform (GCP), including BigQuery, Composer (Airflow), DataProc, Cloud Storage, and related data services
• Understanding of cloud-native application development, distributed systems, and modern software engineering practices
• Knowledge of CI/CD, containerization (Docker), and production deployment concepts
• Good problem-solving, analytical, and communication skills with the ability to work collaboratively in cross-functional teams
• Successful completion of a background verification check, subject to applicable laws and regulations

Core Competencies

Demonstrates expertise in developing and implementing AI solutions, particularly with Large Language Models (LLMs) and GenAI applications, while ensuring compliance with enterprise standards. Proficient in building scalable data pipelines and integrating AI applications with cloud services and enterprise systems.