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Lead AI/ML Engineer

Javen Technologies

  • Chicago, IL
  • 1 day ago

    Highlights

    Agentic AI & Intelligent Automation Design and implement agentic workflows that integrate AI agents with enterprise systems, APIs, knowledge bases, and business processes. Design and implement Retrieval-Augmented Generation (RAG) architectures utilizing enterprise knowledge repositories, vector databases, and semantic search technologies.

    Numbers & Facts

    LocationChicago, IL

    Description

    Job Title: Lead AI/ML Engineer – Generative AI, Agentic AI & AWS
    Duration: 12+ Months with possible extension
    Location: Chicago, IL 

    Job Description: Key Responsibilities:
    AI/ML Engineering & Solution Development
    • Design, develop, test, and deploy machine learning, generative AI, and agentic AI solutions in production environments.
    • Collaborate with data scientists, software engineers, architects, and DevOps teams to build scalable AI products and platforms.
    • Develop and operationalize Large Language Model (LLM) applications using foundation models and enterprise AI services.
    • Design and implement Retrieval-Augmented Generation (RAG) architectures utilizing enterprise knowledge repositories, vector databases, and semantic search technologies.
    • Build and orchestrate AI agents and multi-agent systems capable of autonomous reasoning, planning, workflow execution, and decision support.
    • Develop prompt engineering frameworks, evaluation methodologies, and continuous optimization processes to improve AI application quality and reliability.
    AI Platform Engineering & MLOps
    • Build, test, deploy, and maintain AI/ML and Generative AI pipelines on AWS and Databricks.
    • Create automated workflows for data ingestion, preparation, feature engineering, model training, model deployment, prompt optimization, and model monitoring.
    • Implement CI/CD, MLOps, and LLMOps practices for scalable deployment and lifecycle management of AI solutions.
    • Develop AI observability and monitoring capabilities to measure model performance, drift, hallucinations, latency, cost, and business outcomes.
    • Manage and optimize production AI systems to ensure reliability, security, scalability, and regulatory compliance.
    • Continuously evaluate emerging AI technologies, frameworks, and foundation models to improve enterprise AI capabilities.
    Agentic AI & Intelligent Automation
    • Design and implement agentic workflows that integrate AI agents with enterprise systems, APIs, knowledge bases, and business processes.
    • Develop intelligent automation solutions that streamline operational workflows and improve business efficiency.
    • Build human-in-the-loop review processes and governance controls for AI-assisted decision-making systems.
    • Implement tool-using agents capable of interacting with enterprise applications, databases, and external services while maintaining security and compliance standards.
    AI Governance & Responsible AI
    • Develop and maintain documentation, standards, and governance processes for AI and ML solutions.
    • Ensure AI solutions adhere to Responsible AI principles including transparency, explainability, fairness, security, privacy, and compliance.
    • Partner with risk, security, legal, and governance stakeholders to establish enterprise AI controls and monitoring frameworks.
    • Support model validation, auditability, and explainability requirements for AI-powered applications.
    Leadership & Strategy
    • Serve as a technical leader and mentor for engineers, data scientists, and AI practitioners.
    • Contribute to the organization's AI strategy, architecture standards, and technology roadmap.
    • Identify opportunities where AI, Generative AI, and intelligent automation can create measurable business value.
    • Communicate complex AI concepts, risks, opportunities, and recommendations to technical and business audiences.

    Education & Experience
    • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Artificial Intelligence, or a related field.
    • 7+ years of experience in Machine Learning Engineering, AI Engineering, MLOps, Software Engineering, or related disciplines.
    • 3+ years of hands-on experience deploying AI/ML solutions in cloud environments.
    • Demonstrated experience delivering Generative AI, LLM, RAG, or agent-based solutions in production.
    Technical Qualifications
    • Strong knowledge of AWS AI/ML services including SageMaker, Bedrock, Lambda, Step Functions, CloudFormation, ECS/EKS, and related services.
    • Experience building and deploying machine learning and generative AI applications in production.
    • Proficiency with LLM frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent orchestration frameworks.
    • Experience designing Retrieval-Augmented Generation (RAG) architectures and integrating vector databases.
    • Experience implementing AI agents, agentic workflows, and intelligent automation solutions.
    • Proficiency in Python and related AI/ML libraries and frameworks.
    • Experience with containerization and orchestration technologies such as Docker and Kubernetes.
    • Knowledge of CI/CD, MLOps, LLMOps, model monitoring, and AI observability practices.

    Knowledge, Skills, Abilities and Behaviors
    • Deep understanding of machine learning, deep learning, generative AI, foundation models, and agentic AI architectures.
    • Strong knowledge of software engineering principles, DevSecOps, MLOps, and LLMOps best practices.
    • Ability to architect scalable, secure, and resilient AI platforms and intelligent systems.
    • Experience evaluating and implementing emerging AI technologies and frameworks.
    • Ability to analyze complex business problems and apply AI solutions that generate measurable business value.
    • Strong understanding of responsible AI, governance, explainability, and risk management principles.
    • Excellent communication skills with the ability to explain advanced AI concepts to technical and non-technical audiences.
    • Self-starter who can independently drive AI initiatives from concept through production deployment.
    • Hands-on technologist capable of influencing strategy while remaining engaged in solution delivery.
    • Passion for innovation and continuous learning in the rapidly evolving AI landscape.
    • Ability to mentor and develop engineering talent while fostering an AI-first culture across the organization.

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