AI Engineer

Job Description: AI Engineer

Company: Ariqt Global Technologies

Experience: 2–5 Years

Employment Type: Full-time

Location: Hyderabad, Telangana.

About Ariqt Global Technologies At Ariqt Global Technologies, we are passionate about building innovative, AI-powered solutions that solve real-world business problems. We are looking for a passionate Software Engineer – AI who loves coding, enjoys experimenting with emerging technologies, and is excited about building intelligent, scalable, and production-ready AI applications.

If you are enthusiastic about LLMs, Agentic AI, AI-powered engineering, and next-generation software architecture, and have an entrepreneurial mindset to turn ideas into impactful products, we would love to have you on our team.

Role Overview

We are seeking a highly motivated Software Engineer – AI with 2–5 years of software development experience and a strong interest in AI engineering. The ideal candidate will have hands-on experience with Large Language Models (LLMs), Agentic AI, AI-assisted development tools, and modern software engineering practices.

You will be responsible for designing, developing, and deploying AI-powered applications, building intelligent agents, integrating AI models into enterprise solutions, and leveraging AI tools to accelerate software development and innovation.

We value engineering excellence, ownership, curiosity, and a passion for coding over familiarity with any one specific technology.

Key Responsibilities

• AI & LLM Engineering: Design, develop, and integrate LLM-powered applications using models and frameworks such as OpenAI, Anthropic, LangChain, LangGraph, and other emerging AI technologies.
• Agentic AI Development: Build AI agents, multi-agent workflows, tool-calling systems, and autonomous AI solutions to automate complex business processes.
• AI-Assisted Software Engineering: Leverage AI coding assistants and development tools such as GitHub Copilot, Cursor, Claude, and other AI-powered tools to improve developer productivity and code quality.
• Harness Engineering: Develop and maintain the infrastructure, tools, evaluation loops, guardrails, context management, and feedback mechanisms that enable AI agents and coding systems to operate reliably.
• Software Development: Write clean, maintainable, scalable, and production-ready code while following industry best practices and modern software development principles.
• Solution Architecture: Contribute to designing end-to-end technical solutions, including system architecture, APIs, microservices, integrations, and cloud-native applications.
• Engineering Best Practices: Apply SOLID principles, design patterns, clean architecture, CI/CD, automated testing, and secure coding practices.
• AI Integration: Build RAG-based applications, vector search solutions, knowledge retrieval systems, and AI-driven automation workflows.
• Problem Solving: Analyze complex technical and business challenges and develop practical, innovative, and scalable solutions.
• Collaboration & Ownership: Work closely with cross-functional teams, product stakeholders, and leadership to translate business requirements into technical solutions.
• Continuous Innovation: Stay updated with emerging AI technologies, experiment with new tools and frameworks, and proactively identify opportunities to leverage AI.

Required Technical Skills Programming & Software Engineering

• Strong proficiency in Python and/or JavaScript/TypeScript.
• Solid understanding of object-oriented programming, data structures, algorithms, and problem-solving.
• Good knowledge of REST APIs, backend development, databases, and application integration.
• Experience with Git, version control, debugging, testing, and CI/CD practices.

LLMs & Generative AI

• Hands-on knowledge of LLMs, including GPT, Claude, Gemini, Llama, or similar models.
• Understanding of prompt engineering, structured outputs, function calling, and context management.
• Experience with LLM frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
• Knowledge of Retrieval-Augmented Generation (RAG), embeddings, vector databases, and semantic search.
• Understanding of LLM evaluation, hallucination mitigation, latency, and cost optimization.

Agentic AI & AI Engineering

• Understanding of Agentic AI architectures, AI agents, tool use, planning, memory, and orchestration.
• Exposure to multi-age…