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AI Engineer- Reading, PA | Onsite | In-Person Client Interview

KeylentReading, PA🇺🇸United States

Why this role stands out

This AI Engineer role offers a fantastic opportunity to build cutting-edge AI and machine learning solutions using modern frameworks and AWS services, with significant potential for professional growth. If you are a motivated mid-senior engineer passionate about agentic AI and multi-agent systems, you will thrive in this impactful position. Don't miss out on the chance to apply and contribute to innovative projects.

Quick overview

SeniorityMid Senior
Work modeOn Site
LocationReading, PA, United States
DockerAWSMLOpsMachine LearningGenerative AIGitHub ActionsKubernetesPythonStakeholder ManagementTerraform

Job description

Reading, PA | Onsite | In-Person Client Interview
Job Description: AI Engineer
Position Summary
We are seeking a highly motivated AI Engineer to design, develop, and deploy next-generation AI and machine learning solutions across the enterprise. The ideal candidate will have hands-on experience building intelligent applications, agentic AI systems, and scalable machine learning pipelines utilizing AWS cloud services and modern AI frameworks. This role requires a strong foundation in software engineering, machine learning, multi-agent architectures, and cloud-native AI services, along with a passion for staying current with rapidly evolving AI technologies.
Key Responsibilities
  • Design, develop, and deploy AI-powered applications and agentic workflows using Python (3.11+) and modern software engineering best practices.
  • Build, train, evaluate, and operationalize machine learning models using Amazon SageMaker.
  • Develop and maintain end-to-end ML workflows, including feature engineering, model training, deployment, monitoring, and lifecycle management.
  • Utilize Dataiku for data preparation, analytics, feature engineering, and model development activities.
  • Architect and implement agentic AI solutions leveraging AWS Bedrock AgentCore, including:
    • Agent Runtime
    • Memory Layer
    • Identity Layer
    • Tool Gateway Integration
  • Design and implement multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, or similar orchestration platforms.
  • Integrate external tools, APIs, and enterprise systems using Model Context Protocol (MCP) and modern agent integration patterns.
  • Collaborate with business stakeholders, data scientists, architects, and engineering teams to translate business requirements into scalable AI solutions.
  • Optimize AI applications for performance, reliability, security, and cost efficiency in AWS environments.
  • Develop reusable components, libraries, and deployment frameworks that accelerate enterprise AI adoption.
  • Monitor production AI systems, troubleshoot issues, and implement continuous improvements.
  • Evaluate emerging AI technologies, frameworks, and cloud services to recommend innovative solutions and best practices.
Required Qualifications
Education
  • Bachelor’s degree in Computer Science, Data Science, Engineering, Information Technology, or related field.
  • Master's degree preferred.
Technical Skills
  • Strong hands-on experience developing production-grade applications using Python 3.11+.
  • Experience designing and implementing agentic AI systems and intelligent automation workflows.
  • Hands-on experience with Amazon SageMaker for machine learning model development, training, deployment, and monitoring.
  • Proficiency in Dataiku for data preparation, analytics, and machine learning workflows.
  • Strong understanding of:
    • Machine Learning algorithms
    • Model evaluation techniques
    • Feature engineering
    • MLOps practices
    • Model lifecycle management
  • Experience working with AWS Bedrock AgentCore, including Agent Runtime, Memory, Identity, and Tool Gateway capabilities.
  • Knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and agent-based architectures.
  • Experience with multi-agent orchestration frameworks such as:
    • LangGraph
    • CrewAI
    • AutoGen
    • Equivalent agent orchestration platforms
  • Strong understanding of Model Context Protocol (MCP) and agent tool integration patterns.
  • Experience integrating enterprise APIs, databases, and third-party services into AI workflows.
  • Familiarity with AWS cloud architecture, serverless services, and scalable application design.
Certifications
Candidates possessing any of the below certifications is a plus:
  • AWS Certified Solutions Architect – Associate
  • AWS Certified Developer – Associate
  • AWS Certified AI Practitioner
Preferred Qualifications
  • Experience with Generative AI, RAG architectures, vector databases, and knowledge retrieval systems.
  • Knowledge of AWS Bedrock foundation models and enterprise AI deployment patterns.
  • Familiarity with framework ecosystems such as LangChain, LlamaIndex, and OpenAI-compatible tooling.
  • Experience implementing AI governance, security, observability, and responsible AI practices.
  • Exposure to DevOps/MLOps tools including CI/CD pipelines, GitHub Actions, Terraform, Docker, and Kubernetes.
  • Experience working in large-scale enterprise environments.
Soft Skills
  • Strong analytical and problem-solving abilities.
  • Excellent verbal and written communication skills.
  • Ability to work independently and collaboratively within cross-functional teams.
  • Curiosity and willingness to continuously learn emerging AI technologies, frameworks, and evolving cloud services.
  • Strong stakeholder management and business partnering skills.

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