AI Engineer - Mid-Level

About the Opportunity

KRDL USA is seeking a hands-on AI Engineer to develop and integrate AI solutions for a client engagement. This role involves Python, large language model (LLM) applications, AI agents, Model Context Protocol (MCP) servers, API integrations, cloud technologies, and Cisco Webex Contact Center.

The ideal candidate will have experience building AI applications and Webex AI agents, connecting enterprise systems, and delivering reliable solutions that improve business workflows and customer interactions.

This is a mid-level individual contributor role with opportunities to collaborate with technical leaders, architects, and business stakeholders. Experience with Retrieval-Augmented Generation (RAG) and AI solution architecture is a strong plus.

AI Engineer — Mid-Level

Location: Lincolnshire, IL

Work Arrangement: To be confirmed

Employment Type: Full Time / Client Consulting Assignment

Key

Responsibilities

• Design, develop, and deploy machine learning models and AI-driven applications
• Collaborate with cross-functional teams to define AI/ML requirements and deliver scalable solutions
• Build and maintain data pipelines to support model training, evaluation, and deployment
• Monitor and optimize model performance, accuracy, and efficiency in production environments
• Research and implement state-of-the-art AI/ML techniques and frameworks
• Contribute to code reviews, documentation, and engineering best practices
• Participate in agile development cycles and sprint planning

AI and LLM Application Development

• Develop, test, and maintain AI applications using Python and LLM APIs.
• Build conversational assistants, document analysis solutions, knowledge retrieval capabilities, and workflow automation.
• Design and refine prompts, structured outputs, context management, and model interactions.
• Evaluate implementation options based on response quality, latency, security, and cost.

Webex Contact Center and Webex AI Agents

• Build, configure, test, and enhance AI agents for Cisco Webex Contact Center.
• Develop conversational flows that support customer self-service and defined business use cases.
• Integrate Webex AI agents with enterprise APIs, business applications, and knowledge sources.
• Implement escalation and handoff to human agents, preserving relevant interaction context.
• Collaborate with contact center stakeholders to clarify routing, workflow, and customer experience requirements.
• Test conversational behavior, integration reliability, fallback handling, and end-to-end customer journeys.
• Monitor interactions and recommend improvements to response quality and task completion.

AI Agents and MCP Integration

• Build AI agents that use approved tools and APIs to complete defined tasks.
• Implement tool calling, workflow orchestration, state management, and appropriate human approval steps.
• Develop or integrate MCP servers that expose authorized tools, resources, and enterprise services to AI applications.
• Implement authentication, authorization, input validation, error handling, and logging.
• Test tool permissions, agent behavior, and recovery from failures.

RAG and Data Integration

• Develop document ingestion, extraction, cleaning, chunking, and embedding workflows.
• Integrate vector databases or search platforms to support semantic retrieval.
• Improve retrieval relevance, source attribution, and response groundedness.
• Connect AI applications to structured and unstructured data sources while respecting access permissions.

APIs, Cloud, and Deployment

• Build and consume REST APIs using frameworks such as FastAPI or Flask.
• Integrate AI services with enterprise applications, databases, and third-party platforms.
• Deploy and support applications using approved Azure, AWS, or Google Cloud services.
• Contribute to containerization, CI/CD pipelines, configuration management, and secure secrets handling.
• Monitor application performance, availability, and resource consumption.

Quality, Security, and Collaboration

• Create meaningful tests and evaluation datasets for AI features.
• Assess response accuracy, tool-use correctness, reliability, latency, and operating costs.
• Address risks such as prompt injection, sensitive data exposure, and unauthorized tool execution.
• Participate in code reviews, troubleshooting, release validation, and Agile delivery.
• Maintain technical documentation and communicate progress, dependencies, and issues clearly.

Requirements

• 2–4 years of experience in AI/ML engineering or a related software engineering role
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn
• Experience with NLP, computer vision, or generative AI is a strong plus
• Familiarity with cloud platforms (AWS, Azure, or GCP) and MLOps practices
• Strong understanding of data structures, algorithms, and statistical modeling
• Experience with SQL/NoSQL databases and data preprocessing techniques
• Strong problem-solving skills and ability to work both independently and collaboratively

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related discipline, or equivalent practical experience.
• Typically 2–5 years of relevant software engineering experience, including hands-on development of AI/ML or LLM-based applications.
• Strong Python programming, debugging, and problem-solving skills.
• Practical experience integrating LLMs into applications.
• Hands-on experience with Cisco Webex Contact Center and building Webex AI agents.
• Experience developing conversational flows and integrating AI agents with backend APIs or business systems.
• Experience building or consuming REST APIs and working with JSON.
• Familiarity with AI agent tool calling and workflow orchestration.
• Understanding of MCP concepts and how MCP servers connect AI applications to tools and resources.
• Working knowledge of SQL, data processi