Gen AI Engineer

Job Title: Generative AI Engineer (Agentic AI)
Location: Hanover, NJ (Onsite)

Role Overview:

We are looking for a highly skilled Generative AI Engineer specializing in Agentic AI systems to design and deliver advanced, enterprise-grade AI solutions. This role focuses on building intelligent, autonomous, and multi-agent systems using modern LLM frameworks and cloud-native technologies.

The ideal candidate combines deep expertise in Python development, hands-on experience with agent orchestration frameworks, and strong exposure to AWS AI and data services. You will play a key role in architecting scalable, secure, and explainable AI workflows that solve complex business problems.

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Key Responsibilities:

• Design, develop, and deploy agentic AI solutions using modern frameworks such as LangChain and LangGraph
• Build and orchestrate multi-agent systems leveraging LLMs (e.g., Claude via Amazon Bedrock) for reasoning, planning, and execution
• Develop robust Python-based services, APIs, and pipelines for AI-driven automation and GenAI use cases
• Integrate AI agents with AWS data services (e.g., Glue, Athena, S3) for data retrieval, processing, and analytics
• Implement advanced AI patterns including Retrieval-Augmented Generation (RAG) and tool-enabled agents
• Ensure solutions are scalable, secure, auditable, and production-ready
• Collaborate with cross-functional teams to translate business requirements into agent-based AI architectures
• Contribute to technical architecture, reusable frameworks, and best practices for GenAI ecosystems
• Support model lifecycle management, including prompt versioning, observability, evaluation, and performance optimization

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Core Technical Skills

< data-start="1976" data-end="2010">Programming & Development

• Strong proficiency in Python with experience writing production-grade, maintainable code
• Experience building APIs, microservices, and backend systems for AI applications
• Solid understanding of object-oriented design, asynchronous programming, and modular architectures

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< data-start="2310" data-end="2355">Agent Frameworks & LLM Orchestration

• Hands-on experience with:
  • LangChain
  • LangGraph
  • Deep agent frameworks
• Proven experience building:
  • Autonomous agents
  • Multi-agent systems
  • Stateful and event-driven workflows
• Strong understanding of:
  • Agent orchestration and execution graphs
  • Memory management and planning systems
  • Tool integration and agent lifecycle

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< data-start="2734" data-end="2772">Generative AI & LLM Expertise

• Strong foundation in Generative AI concepts, including:
  • Prompt engineering and orchestration
  • Reasoning techniques (e.g., chain-of-thought)
  • Tool-augmented LLM workflows
  • Retrieval-Augmented Generation (RAG)
• Hands-on experience working with Claude models via Amazon Bedrock
• Familiarity with:
  • Model evaluation techniques
  • Hallucination mitigation strategies
  • Response quality and reliability optimization

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< data-start="3232" data-end="3267">AWS Cloud & Data Ecosystem

• Strong experience with AWS services such as:
  • Amazon Bedrock (LLM hosting and orchestration)
  • AWS Glue (ETL and data pipelines)
  • Amazon Athena (querying structured/unstructured data)
  • Amazon S3 (data lakes and document storage)
• Experience designing and deploying cloud-native AI solutions
• Understanding of IAM, security best practices, logging, and cost optimization

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Agentic AI Expertise (Must-Have)

• Proven experience building AI agents from scratch, not just integrating APIs
• Ability to design intelligent agents with:
  • Goal-driven reasoning
  • Tool invocation capabilities
  • Memory and state management
  • Multi-step task decomposition
• Experience implementing:
  • Multi-agent collaboration and orchestration patterns
  • Agents interacting with APIs, databases, files, and enterprise data systems
• Familiarity with:
  • Agent observability and monitoring
  • Failure handling and recovery mechanisms
  • Explainability and transparency in AI systems
• Strong ability to translate business workflows into agent-based execution models

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Preferred Qualifications:

• Experience in enterprise-scale AI deployments
• Exposure to MLOps / LLMOps practices
• Strong problem-solving and system design skills
• Excellent communication and collaboration abilities