Artificial Intelligence Engineer

Role Overview

We are seeking a highly skilled Senior AI Engineer to design, build, and scale next-generation AI applications leveraging Large Language Models (LLMs), Agentic AI frameworks, Retrieval-Augmented Generation (RAG), and cloud-native architectures. The ideal candidate will possess strong software engineering expertise, hands-on experience with AI orchestration frameworks, and a proven ability to take AI products from prototype through production deployment.

As a Senior AI Engineer, you will work closely with product teams, architects, data engineers, and business stakeholders to deliver scalable, secure, and production-ready AI solutions that drive business value.

Key Responsibilities

AI Solution Development

• Design and develop enterprise-grade AI applications leveraging LLMs and Generative AI technologies.
• Build and deploy Agentic AI workflows using frameworks such as LangChain, LangGraph, and LlamaIndex.
• Design and implement Retrieval-Augmented Generation (RAG) architectures for knowledge-driven AI solutions.
• Develop intelligent agents capable of tool calling, memory management, reasoning, and workflow orchestration.
• Integrate AI services with enterprise systems through RESTful APIs and microservices.

Software Engineering & Platform Development

• Develop scalable backend services using Python and asynchronous programming patterns.
• Design reusable AI components, SDKs, and services to accelerate solution delivery.
• Implement robust APIs with authentication, authorization, monitoring, and logging capabilities.
• Ensure software quality through unit testing, integration testing, and CI/CD pipelines.

AI Infrastructure & Cloud Engineering

• Deploy and manage AI applications using Docker and Kubernetes.
• Build cloud-native AI solutions on AWS, Azure, or Google Cloud Platform.
• Optimize model serving, inference performance, scalability, and cost efficiency.
• Implement observability, monitoring, and reliability practices for AI workloads.

Vector Search & Knowledge Systems

• Design and optimize vector database solutions using Pinecone, Qdrant, Chroma, or similar technologies.
• Develop embedding pipelines, document indexing strategies, and semantic search capabilities.
• Implement data retrieval, ranking, and contextual grounding mechanisms to improve AI response quality.

Leadership & Stakeholder Collaboration

• Lead technical discussions, architecture reviews, and solution design workshops.
• Mentor junior engineers and contribute to AI engineering best practices.
• Collaborate with cross-functional teams to transform business requirements into scalable AI solutions.
• Drive end-to-end delivery from proof of concept through production rollout and operational support.