Enterprise AI Architect

ABOUT THE ROLE

We are looking for an Agentic AI Engineer to design, build, execute, test, and orchestrate autonomous AI agent systems that operate across complex, multi-step workflows. You will work at the intersection of large language models, tool-use frameworks, and enterprise data pipelines to deliver reliable, production-grade agentic solutions.

LLM Orchestration Agent Design Tool Use Prompt Multi-agent Engineering Systems

KEY RESPONSIBILITIES

• Design and implement agentic AI systems (single-agent and multi-agent) with tool usage, memory management, and fallback mechanisms.
• Build production-grade AI agents using frameworks such as LangGraph, AutoGen, CrewAI, or custom LLM orchestration layers.
• Implement agent reasoning loops including planning, tool selection, execution, observation, reflection, and re-planning with safety guardrails.
• Develop prompt engineering and context engineering strategies for reliable, grounded, and enterprise-ready LLM outputs.
• Design agent orchestration workflows including task routing, parallel execution, retries, state management, and human-in-the-loop escalation.
• Build evaluation frameworks for LLMs and AI agents including automated testing, adversarial testing, hallucination detection, and performance benchmarking.
• Implement retrieval and grounding architectures using vector databases, embeddings, semantic search, and knowledge graphs for contextual accuracy.
• Design scalable memory, caching, and context management layers to optimize token consumption, latency, and performance.
• Ensure observability of AI agent systems by tracing LLM calls, tool usage, prompts, token utilization, and decision paths using monitoring frameworks.
• Apply enterprise AI security and governance controls including prompt injection defense, access control, secure tool execution, and responsible AI practices.
• Optimize AI agent systems for scalability, reliability, latency, throughput, and production cost efficiency.
• Build CI/CD and MLOps pipelines for AI agent workflows including versioning, automated testing, deployment, and rollback strategies.
• Integrate AI agents with enterprise systems, APIs, databases, and cloud platforms to automate end-to-end business workflows.
• Design continuous feedback and learning loops using production traces, telemetry, and evaluation signals to improve AI agent quality and performance.
• Experience with Model Context Protocol (MCP) systems to design database connections, integrate APIs, and enable secure tool orchestration for AI agents.
• Hands-on experience in fine-tuning LLMs for domain-specific applications using LoRA, PEFT, QLoRA, RLHF, instruction tuning, and other parameter-efficient adaptation techniques.
• Collaborate with data governance, architecture, security, and engineering teams to establish enterprise AI standards and best practices.
• Stay current with emerging agentic AI frameworks, LLM research, semantic AI technologies, and enterprise AI deployment best practices.
• Define infrastructure, networking, storage, compute, and deployment architectures for cloud and hybrid environments.
• Collaborate with application, data, AI, security, and infrastructure teams to establish enterprise architecture standards and roadmaps.
• Provide technical leadership, architecture reviews, solution guidance, and best practices across engineering teams.
• Support AI/ML and Agentic AI platform integration with enterprise applications, data platforms, APIs, and cloud infrastructure.
• Optimize enterprise systems for performance, reliability, latency, observability, and operational efficiency.
• Architect microservices, event-driven systems, distributed computing, and API-based integration solutions.

REQUIRED SKILLS & EXPERIENCE

• Minimum 5 + years of AI engineering experience, including 3+ years working with LLMs, Generative AI, and agentic AI systems in production.
• Hands-on experience designing agentic AI architectures including ReAct, plan-and-execute, reflection loops, multi-agent orchestration, and tool-use patterns.
• Strong proficiency in Python and experience with frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Strong understanding of prompt engineering, context engineering, structured outputs, and grounding strategies for enterprise AI applications.
• Experience building AI integrations with REST APIs, databases, vector stores, SQL executors, and enterprise applications.
• Hands-on experience with RAG architectures, embeddings, vector databases, semantic search, and knowledge graphs.
• Familiarity with LLM evaluation frameworks including RAGAS, adversarial testing, hallucination detection, and LLM-as-a-judge patterns.
• Strong understanding of AI security and governance including prompt injection defense, secure tool execution, access control, and responsible AI practices.
• Experience with MLOps and observability tools such as MLflow and Weights & Biases.
• Strong experience designing memory, caching, and context management layers for scalable agentic AI systems with token cost optimization strategies.
• Hands-on experience with LLM fine-tuning using LoRA, PEFT, QLoRA, RLHF, and instruction tuning techniques.
• Experience with MCP systems for secure tool orchestration, API integration, and enterprise connectivity.
• Strong experience in enterprise system architecture, distributed systems, cloud platforms, microservices, API integrations, and scalable application design.
• Hands-on expertise with cloud and DevOps technologies including Amazon Web Services, Microsoft Azure, Google Cloud, Docker, Kubernetes, CI/CD, monitoring, and observability frameworks.
• Strong understanding of security architecture, high availability, performance optimization, disaster recovery, and enterprise integration patterns for modern AI and data-driven platforms.

NICE TO HAVE

• Experience with multi-agent systems and inter-agent communication protocols.
• Exposure to data lineage, metadata management, or data catalog systems.
• Contributions to open-source agentic AI projects.
• Hands-on experience with Java, Scala, PySpark, and COBOL development.

WHAT WE OFFER

• Opportunity to build frontier agentic AI systems on real enterprise data.
• Collaborative environment with data engineers, AI researchers, and product teams.
• Competitive salary and flexible working arrangements.