Artificial Intelligence Engineer

We are seeking a highly skilled Agentic AI Engineer / Applied AI Engineer to design, build, and deploy production-grade AI systems that go beyond conversational experiences and execute complex business workflows autonomously.

The ideal candidate will have strong experience in LLM-based agents, Retrieval-Augmented Generation (RAG), multi-agent systems, AI automation, backend engineering, and full-stack AI product development. This role requires the ability to translate complex user intent into reliable, scalable, and observable automated actions., * Experience building Agentic AI platforms or autonomous AI workflows in production.

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• Experience with financial services, fraud detection, risk management, compliance, or enterprise automation.

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• Strong understanding of LLM reasoning, retrieval, memory, planning, tool use, and agent evaluation.

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• Experience taking AI products from prototype/MVP through production deployment and ongoing optimization.

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• Experience building scalable, secure, and reliable enterprise AI applications., The ideal candidate combines AI/ML expertise with strong software engineering and product development skills. You should be comfortable moving from an AI architecture concept to a working production system, building the underlying APIs and infrastructure, integrating LLMs and retrieval systems, and implementing the monitoring and guardrails necessary to make autonomous AI reliable in real-world enterprise environments.

• Design and architect production-grade Agentic AI systems capable of reasoning, planning, decision-making, and executing business workflows.

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• Build LLM-powered agents and multi-agent workflows using modern AI frameworks and orchestration patterns.

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• Develop scalable RAG pipelines, including document ingestion, embedding, vector search, retrieval, ranking, and contextual generation.

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• Integrate LLMs with enterprise systems through backend APIs, tools, function calling, and event-driven architectures.

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• Build AI applications using OpenAI and other foundation models, optimizing prompts, agent behavior, context management, and tool usage.

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• Develop reliable AI systems incorporating memory, evaluation, observability, guardrails, security, and human-in-the-loop workflows.

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• Architect and develop backend services using Python and FastAPI.

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• Build full-stack AI products and internal platforms using React and TypeScript.

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• Design event-driven and asynchronous workflows capable of supporting autonomous AI agents at scale.

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• Implement semantic and vector search solutions using technologies such as Pinecone and FAISS.

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• Develop AI/ML systems for areas including fraud detection, risk scoring, anomaly detection, compliance automation, and intelligent business operations.

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• Establish evaluation frameworks and monitoring systems to measure agent accuracy, reliability, latency, cost, and production performance.

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• Deploy and operate AI applications using AWS, Docker, Kubernetes, and modern MLOps practices.

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• Collaborate with product, engineering, data, and business teams to identify opportunities for AI-driven automation and intelligent workflows.