AI Solutions Engineer

AI & Machine Learning Development

• Design, build, train, evaluate, and deploy machine learning and deep learning models.

• Develop Generative AI solutions using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral.

• Implement Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, and AI agent frameworks.

• Build NLP, recommendation systems, forecasting, predictive analytics, and intelligent automation solutions.

• Optimize model performance, scalability, latency, and cost.

Software Engineering & Solution Development

• Develop production-grade AI applications using Python and modern software engineering practices.

• Build APIs, microservices, and AI-powered enterprise applications.

• Integrate AI services with enterprise systems, business applications, and data platforms.

• Apply coding standards, automated testing, CI/CD, and version control best practices.

MLOps & AI Operations

• Design and implement MLOps pipelines for model development, deployment, monitoring, and lifecycle management.

• Automate model training, validation, testing, and deployment processes.

• Monitor model performance, data drift, hallucinations, and operational metrics.

• Support continuous improvement and reliability of AI platforms.

Cloud & Platform Engineering

• Develop AI solutions on Azure, AWS, or Google Cloud platforms.

• Leverage cloud-native AI services, containerization, Kubernetes, and serverless technologies.

• Build scalable architectures supporting enterprise AI workloads and real-time inference.

AI Governance & Security

• Ensure compliance with Responsible AI, security, privacy, and regulatory requirements.

• Implement model governance, explainability, bias mitigation, and risk management practices.

• Maintain standards for secure design, deployment, and operation of AI solutions.