AI/ML Engineer - Generative AI & Agentic Systems

Job Description:

AI/ML Engineer -

Generative AI & Agentic Systems Role Overview:

We are seeking a highly skilled AI / ML Engineer with strong expertise in Generative AI, Large Language Models (LLMs), and Agentic AI systems to design, develop, and deploy intelligent applications at scale. The ideal candidate should possess hands-on experience building AI-driven solutions using modern frameworks, implementing production-grade machine learning systems, and integrating advanced language model capabilities into enterprise workflows. Key Responsibilities Design, develop, and deploy scalable AI and machine learning solutions for real-world applications. Build and optimize Generative AI applications leveraging Large Language Models (LLMs). Develop Agentic AI and multi-agent workflows using modern orchestration frameworks. Design and implement Retrieval-Augmented Generation (RAG) architectures with semantic search capabilities. Engineer prompts and optimize LLM outputs for accuracy, reliability, and business outcomes. Fine-tune, evaluate, and benchmark AI models for domain-specific use cases. Develop custom machine learning models for classification, clustering, recommendation systems, and predictive analytics. Perform feature engineering, model selection, experimentation, and optimization. Build end-to-end ML pipelines and manage model lifecycle processes using MLOps practices. Collaborate with cross-functional teams to integrate AI solutions into production environments. Monitor model performance, conduct evaluations, and continuously improve deployed systems. Required Technical Skills AI / Machine Learning Generative AI and Large Language Models (LLMs) Agentic AI and Multi-Agent Systems Retrieval-Augmented Generation (RAG) Semantic Search Architectures Prompt Engineering Fine-Tuning and Model Evaluation Custom Machine Learning Model Development Classification Models Clustering Techniques Recommendation Systems Predictive Analytics Feature Engineering MLOps and Model Lifecycle Management Frameworks & Libraries LangGraph LangChain CrewAI (preferred) AutoGen MCP (Model Context Protocol) OpenAI APIs Anthropic Claude APIs Hugging Face Ecosystem Scikit-Learn XGBoost PyTorch Preferred Qualifications Experience building production-grade AI systems using cloud-native architectures. Understanding of multi-agent orchestration and workflow automation. Experience with model evaluation, observability, and AI system monitoring. Strong software engineering fundamentals and API integration experience. Familiarity with scalable deployment patterns for AI applications. Nice to Have Experience with vector databases and embedding pipelines. Exposure to distributed training or large-scale inference systems. Experience with experimentation frameworks and A/B testing for AI systems. Knowledge of responsible AI practices and model governance. Ideal Candidate Profile Strong analytical and problem-solving skills. Ability to translate business requirements into AI solutions. Comfortable working across research, experimentation, and production environments. Passionate about emerging AI technologies and continuous learning.