AI/ML Engineer
Summary
Design and deploy AI-powered solutions using LLMs, RAG, and NLP to build scalable, secure applications and automate business processes.
We are seeking an experienced AI/ML Engineer to design, develop, and deploy AI-powered solutions that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and machine learning technologies. The ideal candidate will have strong expertise in AI/ML model development, LLM integration, natural language processing (NLP), and responsible AI practices to build scalable, secure, and high-performing AI applications.
Key Responsibilities
Design, develop, and deploy AI/ML solutions using Large Language Models (LLMs), NLP, and machine learning techniques.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines, embeddings, and vector search solutions.
Integrate AI capabilities through LLM APIs and develop multi-agent AI systems to automate business processes.
Fine-tune foundation models and evaluate model performance for accuracy, reliability, and efficiency.
Develop AI workflows, prompts, and orchestration frameworks for enterprise AI applications.
Implement AI governance, security, and responsible AI practices, ensuring compliance with organizational and regulatory standards.
Collaborate with software engineers, data engineers, and business stakeholders to deliver AI-driven solutions.
Monitor, troubleshoot, and continuously improve AI models and production deployments.
Qualifications
Bachelor's degree in Computer Science, Information Technology, Information Systems, Software Engineering, Computer Engineering, Data Science, Artificial Intelligence, or a related field.
Minimum of 5 years of experience in AI/ML model development and deployment.
Strong expertise in LLM API integration, Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and AI application development.
Experience designing multi-agent AI systems, embeddings, vector databases, and semantic search.
Hands‑on experience with model fine‑tuning, evaluation, prompt engineering, and AI optimization.
Strong understanding of responsible AI, AI governance, model security, and ethical AI practices.
Experience deploying AI solutions in cloud or enterprise environments.
Preferred Certifications
AI/ML certifications (e.g., TensorFlow, Microsoft AI Engineer, AWS AI/ML, Google Professional Machine Learning Engineer)
Cloud AI certifications from AWS, Microsoft Azure, or Google Cloud
Other relevant AI or machine learning certifications and training