Artificial Intelligence Developer

Intellics Global Services; India; Hybrid; INR 3,500,000 - 6,000,000; Part time

Job summary

Intellics Global Services seeks a Lead / Generative AI Developer to build and integrate enterprise AI solutions using LLMs, RAG, prompts, APIs, and cloud tech. The role requires 5+ years of experience, with a hybrid model across India.

Responsibilities include developing AI apps, implementing prompt strategies, integrating models via APIs/SDKs, and advancing AI CI/CD workflows in collaboration with data scientists and architects.

Qualifications

• 5+ years of relevant experience in AI/ML and software development.
• Experience with enterprise AI implementations and integration.
• Strong knowledge of LLMs, prompts, and API-enabled architectures.

Responsibilities

• Develop, test, and deploy AI and Generative AI applications.
• Implement prompt engineering techniques for LLM optimization.
• Integrate AI models with enterprise apps using APIs and SDKs.
• Build RAG and LLM-based solutions for knowledge-driven use cases.
• Collaborate with solution architects and data scientists on AI model implementation.
• Perform model testing, evaluation, and tuning to improve accuracy and performance.
• Develop secure, scalable, and reliable AI applications.
• Work with cloud platforms and DevOps pipelines for AI CI/CD workflows.
• Document AI solutions, prompts, and integration patterns.
• Stay updated with emerging AI tools and enterprise practices.

Skills

Generative AI

LLMs

RAG

Prompt engineering

APIs

Cloud platforms

DevOps pipelines

CI/CD

Documentation

Job description

Generative AI Developer / Lead

We are looking for an Lead / Generative AI Developer to develop and integrate enterprise AI solutions using LLMs, RAG, prompt engineering, APIs, and cloud technologies.

Experience: 5+ Years

Job Type: C2H

Work Mode: Hybrid

Location: Open to candidates across India

Key Responsibilities

• Develop, test, and deploy AI and Generative AI applications.
• Implement prompt engineering techniques for LLM optimization.
• Integrate AI models and services with enterprise applications using APIs and SDKs.
• Build and maintain RAG and LLM-based solutions for conversational and knowledge-driven use cases.
• Collaborate with solution architects and data scientists on AI model implementation.
• Perform model testing, evaluation, and tuning to improve accuracy and performance.
• Develop secure, scalable, and reliable AI applications.
• Work with cloud platforms and DevOps pipelines for AI CI/CD workflows.
• Document AI solutions, prompts, and integration patterns.
• Stay updated with emerging AI tools, frameworks, and enterprise AI practices.