Generative AI Engineer

About the Company

We are an AI-first technology partner where cutting-edge innovation meets deep industry expertise. The organization accelerates digital transformation by embedding AI and machine learning into every stage of consulting, delivering scalable and repeatable solutions. We welcome individuals who are curious, innovative, and passionate about shaping the future of intelligent systems.

About the Role

We are seeking exceptional Senior AI Engineers to lead the development and optimization of agentic AI systems powering a flagship agentic process automation platform for regulated industries.

You will architect advanced multi-agent systems that reason, validate, and act autonomously on complex business processes such as invoice processing, RFQ management, and loan approvals—while maintaining strict regulatory compliance. Your work will directly impact enterprise customers by enabling 80%+ reduction in manual effort with 90%+ accuracy .

Core Responsibilities

• Design and architect advanced multi-agent systems using LLM reasoning frameworks to autonomously execute complex, multi-step business processes
• Develop sophisticated prompt engineering strategies and agent orchestration patterns with explainability
• Implement multi-agent patterns including critic agents, observer agents, and task-specific agents using ReVAct (Reason, Validate, Act) frameworks
• Build and optimize LLM reasoning pipelines supporting chain-of-thought, tree-of-thought, and graph-based reasoning
• Design validation frameworks to ensure regulatory compliance and auditable decision trails
• Implement guardrails and inspection systems to detect emergent agent behavior and enforce business rules
• Develop evaluation and benchmarking frameworks to assess accuracy, consistency, and decision quality
• Lead integrations with enterprise systems (APIs, document repositories, email, knowledge bases) using Model Context Protocol (MCP)
• Mentor junior engineers and establish best practices for agentic AI development and governance
• Stay current with advancements in LLMs (GPT-4, Claude, Llama) and agentic frameworks (LangChain, CrewAI, AutoGen, etc.)

Requirements

Must-Have Qualifications

• LLM Reasoning Expertise: Deep understanding of LLM capabilities, limitations, and reasoning techniques (chain-of-thought, few-shot, in-context learning)
• Multi-Agent Architecture: Production experience designing and building multi-agent systems with agent coordination and task decomposition
• Agentic AI Frameworks: Hands-on experience with LangChain, CrewAI, AutoGen, or similar frameworks
• LLM Fine-Tuning & Optimization: Experience adapting LLMs for domain-specific and regulated use cases
• Observability & Debugging: Proficiency with LLM observability tools (LangFuse, LangSmith, or equivalent)
• Python Expertise: Advanced Python skills for AI systems, agent frameworks, and data pipelines
• Production AI Systems: Experience deploying scalable, reliable AI/ML systems with governance and monitoring
• LLM API Integration: Strong experience with OpenAI, Anthropic, AWS Bedrock, or open-source models in production

Nice-to-Have Qualifications

• Experience with document processing and unstructured data extraction (PDFs, contracts, financial documents)
• Knowledge of RLHF or similar agent refinement techniques
• Background in NLP, computational linguistics, or formal reasoning systems
• Familiarity with regulated industry workflows (finance, healthcare, life sciences)
• Experience with knowledge graphs, semantic reasoning, or ontology-driven AI
• Understanding of uncertainty quantification and confidence scoring
• Experience with rule-based or expert systems
• Open-source contributions or research in LLM reasoning or multi-agent systems
• Knowledge of interpretable AI and explainability techniques

What You’ll Work With

• 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 infrastructure on AWS
• Enterprise integration patterns (APIs, email, document repositories)
• Model Context Protocol (MCP) for system integration