AI Engineer - Mid-Level

KRDL USA INC•Lincolnshire, IL

About The Position

KRDL USA is seeking a hands-on, mid-level individual-contributor AI Engineer for a client consulting assignment in Lincolnshire, Illinois. The engineer will develop and integrate AI solutions involving Python, large language models, AI agents, Model Context Protocol (MCP) servers, API integrations, cloud technologies, and Cisco Webex Contact Center. The role collaborates with technical leaders, architects, cross-functional teams, and business stakeholders to deliver scalable, secure, and reliable solutions that improve workflows and customer interactions. Work arrangement is to be confirmed. The position includes developing AI and LLM applications, conversational assistants, document analysis and retrieval capabilities, workflow automation, and Webex Contact Center AI agents. It also involves connecting agents to enterprise systems and knowledge sources, implementing RAG and MCP integrations, deploying and monitoring applications in cloud environments, and evaluating quality, reliability, security, latency, and cost. Experience with RAG and AI solution architecture is a strong plus. The posting states that experience with every listed technology is not required; strong Python and AI development skills, along with hands-on Webex Contact Center and Webex AI agent-building experience, are central. KRDL USA describes itself as a technology company and digital transformation partner focused on AI, cloud computing, enterprise architecture, intelligent automation, and emerging technologies. It promotes a collaborative, inclusive, and growth-oriented environment. Compensation and benefits vary by employment classification, position, location, and eligibility; specific offerings will be confirmed during hiring.

Requirements

2–4 years of experience in AI/ML engineering or a related software engineering role; the posting also specifies typically 2–5 years of relevant software engineering experience.
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related discipline, or equivalent practical experience.
Strong Python programming, debugging, and problem-solving skills.
Practical experience integrating LLMs into applications and hands-on development of AI/ML or LLM-based 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, and understanding of MCP concepts and how MCP servers connect AI applications to tools and resources.
Working knowledge of SQL, data processing, and application integration; experience with SQL/NoSQL databases and data preprocessing techniques.
Experience with at least one major cloud platform: Azure, AWS, or Google Cloud; familiarity with MLOps practices.
Familiarity with Git, testing, and collaborative software development.
Clear communication skills and ability to work independently and collaboratively with technical and business stakeholders.
Strong understanding of data structures, algorithms, and statistical modeling.
Proficiency with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

Nice To Haves

Experience with NLP, computer vision, or generative AI.
Experience developing and deploying RAG applications.
Hands-on experience building MCP servers or integrating MCP clients.
Familiarity with LangChain, LangGraph, LlamaIndex, or comparable tools.
Experience with vector databases or search technologies such as pgvector, Pinecone, OpenSearch, or Azure AI Search.
Experience with contact center workflows, routing, human-agent handoff, and conversational analytics.
Familiarity with speech-to-text, text-to-speech, and voice-based AI applications.
Experience with Docker, CI/CD pipelines, asynchronous Python, or event-driven integrations.
Understanding of AI architecture patterns, scalability, observability, and cost optimization.
Experience supporting enterprise or healthcare-related applications.
Experience with RAG and AI solution architecture.

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 for model training, evaluation, and deployment.
Monitor and optimize model performance, accuracy, and efficiency in production.
Research and implement AI/ML techniques and frameworks.
Contribute to code reviews, documentation, and engineering best practices; participate in Agile development and sprint planning.
Develop, test, and maintain Python applications using LLM APIs, including conversational assistants, document analysis, knowledge retrieval, and workflow automation.
Design and refine prompts, structured outputs, context management, and model interactions; evaluate options for response quality, latency, security, and cost.
Build, configure, test, and enhance AI agents for Cisco Webex Contact Center, including conversational flows for customer self-service and business use cases.
Integrate Webex AI agents with enterprise APIs, business applications, and knowledge sources; implement human-agent escalation and handoff while preserving interaction context.
Work with contact center stakeholders to clarify routing, workflow, and customer experience requirements; test conversational behavior, integrations, fallbacks, and end-to-end journeys.
Monitor contact center interactions and recommend improvements to response quality and task completion.
Build AI agents using approved tools and APIs; 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; implement authentication, authorization, input validation, error handling, and logging.
Test tool permissions, agent behavior, and failure recovery.
Develop document ingestion, extraction, cleaning, chunking, and embedding workflows; integrate vector databases or search platforms for semantic retrieval.
Improve retrieval relevance, source attribution, and response groundedness; connect applications to structured and unstructured data while respecting access permissions.
Build and consume REST APIs, and 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, configuration management, and secure secrets handling.
Monitor application performance, availability, and resource consumption.
Create tests and evaluation datasets; assess response accuracy, tool-use correctness, reliability, latency, and operating costs.
Address risks including prompt injection, sensitive data exposure, and unauthorized tool execution.
Participate in troubleshooting and release validation; maintain technical documentation and communicate progress, dependencies, and issues clearly.