AI/ML Engineer

Senior AI/ML Engineer

Job requirements

Experience Range:- 2–4 years of experience, including at least 2 years specifically building LLM-based applications, RAG systems, or AI agent workflows Key Responsibilities:- Design and build end-to-end AI agent workflows, from initial prompt design through production deployment, ensuring scalable and robust solutions

• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embedding models, retrieval ranking, and context window management to maximize information accuracy and retrieval efficiency
• Build and systematically iterate on prompt engineering layers, testing and refining prompts and chain-of-thought strategies to achieve consistent, high-quality outputs across diverse inputs
• Implement tool orchestration within agent workflows by integrating agents with databases, rule engines, validation systems, and formatting tools to automate complex tasks
• Establish automated quality checks and validation layers to proactively catch issues and ensure high output reliability before human review
• Instrument solutions for measurement, collaborating with data scientists to develop evaluation frameworks and track solution performance against defined targets
• Deploy and maintain AI/ML solutions in production environments, focusing on reliability, monitoring, and edge case handling
• Design and implement feedback loops that capture expert review data and translate it into measurable improvements in agent performance Required Skills:- Advanced proficiency in Python
• Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or similar
• Expertise in prompt engineering and systematic prompt testing
• Deep understanding of RAG architectures, including embedding models, vector stores, retrieval strategies, and re-ranking
• Experience building multi-step agent workflows with tool use, branching logic, and robust error handling
• Experience with production deployment and monitoring of AI/ML solutions
• Experience with data pipeline tools and frameworks (KubeFlow, BentoML, Great Expectations, Evidently AI) Preferred Skills:- Experience with multi-agent orchestration frameworks
• Background in content generation, translation, or document processing solutions
• Familiarity with fine-tuning LLMs or training reward models
• Experience implementing feedback loops or RLHF mechanisms
• Expertise in LLM cost optimization strategies such as model routing, caching, and prompt compression