Technical Consultant-AI Integration
IBM
Bengaluru, Karnataka, India3h agovia LinkedIn
This job at a glance
- Who can apply
- Freshers · Master's degree
- Must know
- REST APIsStakeholder management
- Work
- On-site in Bengaluru
- Pay
- Not stated by the employer.
- Skill demand
- REST APIs49%Stakeholder management32%of 99 similar fresher jobs posted in India this week
- Listed
- 27 Oct 2026 · still open
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About the Role
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.
Key Responsibilities
- 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.
Requirements & Tech Stack
- 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.
Qualifications
- Preferred Education: Master's Degree