Technical Consultant-AI Integration

Introduction A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your Role And Responsibilities

• Hands on experience on AI design patterns multi-agent orchestration and task delegation 2. Demonstrate expertise in Python and Python-based microservice development. 3. Work extensively on LLM (Large Language Model) development. 4. Apply advanced skills in prompt engineering, prompt tuning, creating MCP servers integrated with Tool callings 5. Build and deploy intelligent applications using RAG (Retrieval-Augmented Generation). 6. Possess strong knowledge of agentic frameworks such as LangChain Agents, AutoGen, or similar. 5. Hands-on experience with LangChain and other AI/ML frameworks like Hugging Face, Haystack, and OpenAI SDK. 6 Work hands-on with PyTorch, Pandas, and modern ML/DL frameworks.

Preferred Education Master's Degree

Required Technical And Professional Expertise The Technical Consultant – AI Integration is responsible for implementing, configuring, and customizing AI-driven solutions that integrate seamlessly into enterprise applications and workflows. This role translates solution designs and architectural blueprints into working systems, ensuring that AI services, APIs, and orchestration layers are deployed securely, efficiently, and in compliance with organizational and regulatory standards. The consultant develops connectors, builds Retrieval-Augmented Generation (RAG) pipelines, configures prompts and guardrails, and implements observability, performance tuning, and cost optimization measures. They collaborate closely with architects, data engineers, and business stakeholders to validate requirements, deliver proofs of concept, and harden solutions for production. The role demands strong hands-on expertise in API integration, event-driven patterns, workflow automation, and LLMOps practices, with a focus on reliability, scalability, and responsible AI principles throughout the delivery lifecycle.