A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe.
You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio; including Software and Red Hat.
Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role you'll be encouraged to challenge the norm investigate ideas outside of your role and come up with creative solutions resulting in ground breaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and development opportunities in an environment that embraces your unique skills and experience.
In this role you'll work in one of our IBM Consulting Client Innovation Centers (Delivery Centers) where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world. Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.
As a Cloud AI Developer you will play a key role in designing developing testing and deploying AI-powered applications within modern cloud infrastructures. You will leverage Generative AI (GenAI) Machine Learning (ML) and agentic AI technologies to deliver scalable reliable and secure solutions. This position requires close collaboration with DevOps security and product teams to ensure continuous integration delivery monitoring and optimization of AI-driven systems.
Key Responsibilities
- Design implement and optimize GenAI and agentic AI solutions based on project and business requirements.
- Develop and maintain Retrieval-Augmented Generation (RAG) pipelines and agent workflows.
- Integrate AI agents with external systems and protocols (e.g. MCP A2A).
- Collaborate with DevOps teams to deploy and monitor applications in multi-cloud environments (Azure AWS).
- Optimize system scalability reliability and performance through automation and best practices.
- Implement and maintain application validation testing and guardrailing techniques to ensure responsible AI usage.
- Maintain and improve existing AI/ML applications and pipelines.
- Stay current with emerging trends in AI LLMs and cloud technologies evaluating their potential adoption.
- Document architecture workflows and best practices for knowledge sharing and compliance.
- Collaborate with QA and security teams to design and execute appropriate testing strategies
- Support of implementation of AI/ML-specific testing practices and automation of testing pipelines
- Contribute to cross-functional discussions on security data governance and compliance in AI systems.
- Strong experience with Retrieval-Augmented Generation (RAG).
- Proven knowledge of agentic AI protocols and integrations (e.g. MCP A2A).
- Minimum 3 years of professional experience in Python development.
- Solid understanding of cloud-native application design and deployment (Azure or AWS).
- Hands-on experience with at least one agentic AI framework (e.g. Microsoft AutoGen LangGraph CrewAI Semantic Kernel).
- Familiarity with MLOps/DevOps practices for CI/CD monitoring and testing AI systems.
- Knowledge of containerization and orchestration technologies (e.g. Docker Kubernetes).
- Experience with Microsoft’s PromptFlow and Azure AI services (AI Search AI Foundry Cognitive Services).
- Knowledge of guardrailing techniques for safe and responsible agentic AI deployment.
- Experience fine-tuning LLMs with methods such as LoRA or PEFT.
- Exposure to agentic coding assistants and tools (e.g. Cursor Windsurf Claude).
- Understanding of vector databases (e.g. Pinecone Weaviate FAISS) for RAG pipelines.
- Experience with distributed systems and data pipelines (e.g. Kafka Spark Databricks).
- Familiarity with API design and integration (REST GraphQL).
- Background in applied ML/AI research or open-source AI contributions.