AI Agent Developer

MCI Careers•,

7d•Onsite

About The Position

MCI is a rapidly growing tech-enabled business services company with a significant call center presence and international operations. We offer Customer Experience (CX), Business Process Outsourcing (BPO), and Anything-as-a-Service (XaaS) cloud technology solutions to various industries. Our contact centers utilize both on-site and remote agents, employing advanced technologies to improve customer interactions, scalability, and cost-efficiency. MCI is dedicated to providing professionals with opportunities for career growth, continuous learning, and development within a leading global organization. We are looking for an innovative AI Agent Developer to design and build intelligent AI agents for task automation, workflow orchestration, and business operations enhancement. This role involves creating autonomous and semi-autonomous systems that utilize Large Language Models, enterprise data, and external applications to achieve intelligent outcomes. The ideal candidate will contribute to the advancement of AI-powered automation by developing scalable, reliable, and business-oriented agent solutions. To be considered, candidates must complete a full application on our company careers page, including all screening questions and a brief pre-employment test.

Requirements

Bachelor's Degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, or a related field.
Minimum 3 years of software development experience.
Experience building AI-powered applications, intelligent systems, or workflow automation solutions.
Strong proficiency in Python and API development.
Understanding of Large Language Models (LLMs) and Generative AI technologies.
Experience integrating applications through APIs, web services, and third-party platforms.
Knowledge of agent architectures, orchestration frameworks, and automation concepts.
Experience designing scalable and maintainable software solutions.
Understanding of prompt engineering principles and AI workflow design.
Familiarity with cloud platforms and deployment environments.
Strong troubleshooting, analytical, and problem-solving skills.
Excellent collaboration and communication abilities.

Nice To Haves

Experience with CrewAI, LangGraph, AutoGen, OpenAI Agents, or similar frameworks.
Experience implementing Retrieval-Augmented Generation (RAG) architectures.
Knowledge of vector databases and semantic search technologies.
Experience with workflow automation platforms.
Familiarity with Docker, Kubernetes, and containerized deployments.
Understanding of AI governance and responsible AI principles.
Experience building multi-agent systems.
Exposure to MLOps and AI operations practices.

Responsibilities

Develop intelligent agents that perform tasks, execute workflows, and interact with business systems.
Design and build autonomous and semi-autonomous AI agents.
Develop agent logic, memory, reasoning, and planning capabilities.
Create agent workflows that automate complex business processes.
Support multi-agent collaboration and orchestration solutions.
Enable seamless interaction between AI agents and enterprise systems.
Integrate agents with APIs, databases, and business applications.
Build automated workflows across multiple platforms and technologies.
Develop reusable tools and services that support agent functionality.
Support enterprise-wide AI automation initiatives.
Ensure AI agents operate efficiently, accurately, and reliably.
Monitor agent performance and execution outcomes.
Conduct testing, validation, and troubleshooting activities.
Improve response quality, reliability, and task completion rates.
Optimize agent workflows for scalability and efficiency.
Support the responsible deployment of AI agent solutions.
Implement safeguards and controls for agent operations.
Ensure compliance with organizational security requirements.
Maintain documentation, standards, and operational procedures.
Support AI governance and risk management initiatives.
Drive innovation within AI agent development practices.
Evaluate emerging agent frameworks and technologies.
Research new approaches to AI automation and orchestration.
Recommend enhancements to existing agent architectures.
Contribute to AI best practices and knowledge-sharing initiatives.