2-4 years of experience in AI/ML, with 2 years focused on LLM applications or RAG systems
Advanced proficiency in Python
Hands-on experience with LLM frameworks like LangChain or LlamaIndex
Expertise in prompt engineering and systematic testing
Deep understanding of RAG architectures and retrieval strategies
Experience in deploying and maintaining AI/ML solutions in production
Experience with data pipeline development for AI systems
Responsibilities
Design and build end-to-end AI agent workflows from prompt design to production deployment
Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for precise information retrieval
Build and iterate on prompt engineering layers, testing and refining outputs
Implement tool orchestration within agent workflows for seamless operation
Establish automated quality checks to proactively identify and resolve issues
Collaborate with Data Scientists to develop evaluation frameworks and track performance
Deploy, monitor, and maintain AI/ML solutions in production environments
Design feedback loops to capture expert review data for performance improvements
Benefits
Opportunity to work on cutting-edge AI technologies
Collaborative environment with Data Scientists
Focus on innovative problem-solving and continuous improvement
Access to professional development and certification opportunities
Engagement in impactful projects that shape AI applications
Full Job Description
Senior AI/ML Engineer Job requirements
Experience Range: 2 to 4 years of experience, including at least 2 years specifically focused on developing 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 reliable solutions
Develop and optimize Retrieval-Augmented Generation (RAG) pipelines by implementing chunking strategies, embedding models, retrieval ranking, and context window management for precise information retrieval
Build and iterate on prompt engineering layers, systematically testing and refining prompts and chain-of-thought strategies to deliver consistent outputs across diverse inputs
Implement tool orchestration within agent workflows, integrating agents with databases, rule engines, validation systems, and formatting tools for seamless operation
Establish automated quality checks and validation layers to proactively identify and resolve issues before outputs reach human reviewers
Collaborate with Data Scientists to instrument solutions for measurement, developing evaluation frameworks and tracking solution performance against defined targets
Deploy, monitor, and maintain AI/ML solutions in production environments, ensuring reliability, scalability, and robust error handling
Design and implement feedback loops to 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 or LlamaIndex
Expertise in prompt engineering for systematic testing and iteration
Deep understanding of RAG architectures including embedding models, vector stores, retrieval strategies, and re-ranking
Experience building multi-step agent workflows with tool use and branching logic
Experience deploying and maintaining AI/ML solutions in production environments
Experience with data pipeline development for feeding AI systems
Preferred Skills:
Experience with multi-agent orchestration frameworks
Background in content generation, translation, or document processing solutions
Familiarity with feedback loops, RLHF, or reward model training
Knowledge of multi-modal AI systems including voice-to-text, document understanding, and image analysis
Experience with evaluation frameworks for generative AI and automated scoring
Experience with LLM cost optimization strategies such as model routing, caching, and prompt compression
Desired Qualifications:
Bachelor's degree in Computer Science, Data Science, Information Technology, Statistics, or a closely related discipline
Certification in Machine Learning or Artificial Intelligence (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning - Specialty)
Certification in LLM engineering or generative AI (e.g., DeepLearning.AI Generative AI with LLMs)
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