Machine Learning Engineer / AI

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

As an ML Engineer with Advanced Analytics skills, you will apply your strong understanding of machine learning techniques and their applications to design, develop, and deploy production-ready ML software. You will utilize popular libraries such as scikit-learn, TensorFlow, or PyTorch to ensure that ML solutions are efficient, scalable, and maintainable.

Your Primary Responsibilities Will Include

• Design and Develop ML Software: Apply various ML algorithms, including regression, classification, clustering, and recommender systems, to create production-ready ML software.
• Deploy and Maintain ML Solutions: Ensure that ML solutions are efficient, scalable, and maintainable, and deploy them to meet business needs.
• Apply ML Techniques: Utilize machine learning techniques to solve complex problems and improve system performance.
• Develop Distributed Systems: Design and develop distributed systems and ML components to support large-scale ML applications.
• Implement ML Algorithms: Implement ML algorithms using popular libraries such as scikit-learn, TensorFlow, or PyTorch to drive business outcomes.

Desire years of experience overall IT: strong years of experience.

AI/ML Engineer to design, develop, and deploy cutting-edge AI and RAG/machine learning solutions. Develop custom RAG system using internal data, built pipelines for unstructured content, optimized semantic retrieval, and evaluated performance with key quality metrics. Partner with cross‑functional teams to deploy agentic RAG solutions, worked with leading LLMs, ran experiments to improve models, and stayed current on AI/ML advancements.

Additional Technology Skills Required

• Frameworks: FastAPI, Uvicorn(ASGI Server), Pydantic (Data Validation),Pydantic-Settings (Config Management), LangChain, Hugging Face Transformers.
• Vector Databases: pgVector.
• Testing: pytest 8.4.1
• Deployment: Docker, OpenShift, Jenkins (CI/CD)