Senior AI Engineer - Agentic AI & LLM Reasoning

• Design and architect advanced multi -agent systems using LLM reasoning frameworks capable of autonomously executing complex, multi -step business processes
  
• Develop sophisticated prompt engineering strategies and agent orchestration patterns enabling agentic AI to reason through domain -specific business logic with explainability
  
• Implement various multi -agentic patterns including critic agents, observer agents, and specialized task agents within the ReVAct (Reason, Validate, Act) framework
  
• Build and optimize LLM reasoning pipelines supporting chain -of -thought, tree -of -thought, and graph -based reasoning approaches for complex decision -making
  
• Design validation frameworks ensuring agentic AI decisions maintain regulatory compliance and audit trail requirements in financial services, healthcare, and life sciences
  
• Implement guardrails and inspection systems detecting emergent agent behaviors and enforcing business rule boundaries
  
• Develop evaluation frameworks and benchmarking methodologies for assessing multi -agent system accuracy, consistency, and decision quality
  
• Lead integration with enterprise systems including document repositories, APIs, email systems, and knowledge bases through Model Context Protocol (MCP) standards
  
• Mentor junior engineers and establish best practices for agentic AI development and governance
  
• Stay current with latest LLM advances (GPT -4, Claude, Llama) and agentic frameworks (LangChain, CrewAI, Autogen, etc.)
  

Requirements

• LLM Reasoning Expertise: Deep knowledge of large language model capabilities, limitations, and advanced reasoning techniques (chain -of -thought, few -shot prompting, in -context learning)
  
• Multi -Agent Architecture: Production experience designing and building multi -agent systems with understanding of agent coordination, communication patterns, and task decomposition
  
• Agentic AI Frameworks: Hands -on experience with agentic AI implementations using frameworks like LangChain, CrewAI, AutoGen, or similar; ability to architect complex agent interactions
  
• LLM Fine -Tuning & Optimization: Experience fine -tuning LLMs for domain -specific tasks, adapting models for regulatory compliance, and optimizing for inference efficiency
  
• LangFuse / Observability: Proficiency with LLM observability platforms (LangFuse, LangSmith, or equivalent) for debugging, monitoring, and optimizing multi -agent workflows
  
• Python Expertise: Advanced Python skills for implementing complex AI systems, agent frameworks, and data processing pipelines
  
• Production AI Systems: Experience deploying AI/ML systems at scale with focus on reliability, monitoring, and operational governance
  
• LLM API Integration: Deep experience working with leading LLM APIs (OpenAI, Anthropic, AWS Bedrock, open -source models) in production environments 
  

• Experience with document processing pipelines and information extraction from unstructured data (PDFs, contracts, financial documents)
  
• Knowledge of reinforcement learning from human feedback (RLHF) or similar techniques for refining agent behavior
  
• Background in natural language processing, computational linguistics, or formal reasoning systems
  
• Familiarity with regulated industry workflows (financial services, healthcare, life sciences) and compliance requirements
  
• Experience building knowledge graphs, semantic reasoning systems, or ontology -based AI applications
  
• Understanding of uncertainty quantification and confidence scoring in AI predictions
  
• Prior experience with rule -based systems or expert systems that could inform agentic design
  
• Published research or open -source contributions in LLM reasoning or multi -agent systems
  
• Knowledge of interpretable AI and explainability techniques for regulated environments
  

• Advanced LLM platforms and APIs (GPT -4, Claude, Llama, Bedrock)
  
• Agentic AI frameworks (LangChain, CrewAI, AutoGen, Haystack)
  
• Document processing and knowledge management systems
  
• LLM observability tools (LangFuse, LangSmith)
  
• Multi -tenant SaaS backend infrastructure on AWS
  
• Enterprise integration patterns (APIs, email, document repositories)
  
• Model Context Protocol (MCP) for system integration
  

Benefits

• Learn from Experts: Work directly with former AWS leaders and AI pioneers.
  
• Direct Ownership: Lead high -impact "greenfield" projects from concept to global launch.
  
• Modern Tech: Master the latest Generative AI frameworks and cloud -native architectures.
  
• Real -World Impact: Build mission -critical systems used by major global enterprises.
  
• Rapid Growth: Scale your career quickly in a high -speed
  

Diversity and Inclusion