Artificial Intelligence Engineer

Job Description

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.

• Design and architect
  ** production-grade Agentic AI systems
  ** capable of reasoning, planning, decision-making, and executing business workflows.
• Build
  ** LLM-powered agents and multi-agent workflows
  ** using modern AI frameworks and orchestration patterns.
• Develop scalable
  ** RAG pipelines**, including document ingestion, embedding, vector search, retrieval, ranking, and contextual generation.
• Integrate LLMs with enterprise systems through
  ** backend APIs, tools, function calling, and event-driven architectures**.
• Build AI applications using
  ** OpenAI and other foundation models**, optimizing prompts, agent behavior, context management, and tool usage.
• Develop reliable AI systems incorporating
  ** memory, evaluation, observability, guardrails, security, and human-in-the-loop workflows**.
• Architect and develop backend services using
  ** Python and FastAPI**.
• Build full-stack AI products and internal platforms using
  ** React and Type Script**.
• Design event-driven and asynchronous workflows capable of supporting autonomous AI agents at scale.
• Implement semantic and vector search solutions using technologies such as
  ** Pinecone and FAISS**.
• Develop AI/ML systems for areas including
  ** fraud detection, risk scoring, anomaly detection, compliance automation, and intelligent business operations**.
• Establish evaluation frameworks and monitoring systems to measure
  ** agent accuracy, reliability, latency, cost, and production performance**.
• Deploy and operate AI applications using
  ** AWS, Docker, Kubernetes, and modern MLOps practices**.
• Collaborate with product, engineering, data, and business teams to identify opportunities for
  ** AI-driven automation and intelligent workflows**.

• ** Python**
• ** OpenAI / LLM APIs**
• ** Lang Chain**
• ** Lang Graph**
• ** Lang Smith**
• ** Vector databases and semantic search**
• ** Pinecone / FAISS**
• ** FastAPI**
• ** REST APIs and backend development**
• ** React / Type Script**
• ** Redis**
• ** AWS**
• ** Docker**
• ** MLflow**
• AI/ML evaluation and observability
• Agent orchestration and tool/function calling
• Event-driven architectures and workflow automation

• Experience building
  ** Agentic AI platforms or autonomous AI workflows
  ** in production.
• Experience with
  ** financial services, fraud detection, risk management, compliance, or enterprise automation**.
• Strong understanding of
  ** LLM reasoning, retrieval, memory, planning, tool use, and agent evaluation**.
• Experience taking AI products from
  ** prototype/MVP through production deployment and ongoing optimization**.
• Experience building scalable, secure, and reliable
  ** enterprise AI applications**.

This position is suited for professionals working in or transitioning toward:

• ** Agentic AI Engineering**
• ** AI Automation Engineering**

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.