Technical Consultant-Ai Integration

IBM; Gurugram District; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

IBM India is seeking an experienced Technical Consultant specializing in AI Integration to implement, configure, and customize AI-driven solutions that fit into enterprise applications and workflows.

You will translate designs into working systems with a focus on reliability, scalability, and responsible AI, collaborating with architects, data engineers, and business stakeholders to deliver production-ready solutions while ensuring compliance and cost effectiveness.

Qualifications

• Bachelor's degree required.
• Master's degree preferred.
• 5–7 years of experience in AI integration or related fields.
• Experience with API integration and RAG pipelines.
• Familiarity with LLMOps, observability, and cost optimization.
• Secure deployment in enterprise environments.

Responsibilities

• Implement AI solutions and build RAG pipelines for enterprise apps.
• Configure prompts, guardrails, and connectors for AI services.
• Collaborate with architects, data engineers, and stakeholders.
• Optimize performance, observability, and cost.
• Ensure compliance with organizational and regulatory standards.
• Translate designs into reliable, scalable systems.

Skills

API Integration

Workflow Automation

LLMOps

Secure Deployment

AI Solution Development

Education

Bachelor's degree

Master's degree

Tools

RAG pipelines

Job description

Your role and responsibilities

As a Technical Consultant specializing in AI Integration, you will implement, configure, and customize AI-driven solutions that integrate seamlessly into enterprise applications and workflows. This role requires translating solution designs and architectural blueprints into working systems, ensuring secure, efficient, and compliant deployment of AI services, APIs, and orchestration layers.

Your primary responsibilities will include:

• Implement AI Solutions: Develop connectors, build Retrieval-Augmented Generation (RAG) pipelines, and configure prompts and guardrails to integrate AI services into enterprise applications and workflows.


• Collaborate with Stakeholders: Work closely with architects, data engineers, and business stakeholders to validate requirements, deliver proofs of concept, and harden solutions for production.


• Optimize Performance: Implement observability, performance tuning, and cost optimization measures to ensure reliable and scalable AI solutions.


• Ensure Compliance: Deploy AI services, APIs, and orchestration layers in compliance with organizational and regulatory standards.


• Deliver Solutions: Translate solution designs and architectural blueprints into working systems, focusing on reliability, scalability, and responsible AI principles throughout the delivery lifecycle.

Required education

Bachelor's Degree

Preferred education

Master's Degree

Required technical and professional expertise

• Exposure to API Integration: Hands-on experience with integrating AI services and APIs into enterprise applications and workflows, including developing connectors and configuring prompts and guardrails.


• Experience with Workflow Automation: Familiarity with workflow automation and event-driven patterns, including building Retrieval-Augmented Generation (RAG) pipelines and implementing orchestration layers.


• LLMOps Practices: Exposure to LLMOps practices, including implementing observability, performance tuning, and cost optimization measures to ensure reliable and scalable AI solutions.


• Secure Deployment: Experience with deploying AI services, APIs, and orchestration layers in compliance with organizational and regulatory standards, ensuring secure and efficient deployment.


• AI Solution Development: Experience working with AI solution designs and architectural blueprints, translating them into working systems that integrate seamlessly into enterprise applications and workflows.

Preferred technical and professional experience

• Workflow Automation Expertise: Familiarity with workflow automation and event-driven patterns, including building Retrieval-Augmented Generation (RAG) pipelines and implementing orchestration layers, is beneficial for this role.


• LLMOps Practices Knowledge: Exposure to LLMOps practices, including implementing observability, performance tuning, and cost optimization measures, is advantageous for ensuring reliable and scalable AI solutions.


• Secure Deployment Experience: Experience with deploying AI services, APIs, and orchestration layers in compliance with organizational and regulatory standards is desirable, ensuring secure and efficient deployment.

Years of Experience:

5 - 7