AI Adoption Engineer

Overview

Hybrid
Depends on Experience
Contract - Independent
Contract - W2

Skills

Natural Language Processing
Artificial Intelligence
Large Language Models (LLMs)
Machine Learning (ML)

Job Details


Position Title: AI Adoption Engineer
Location: Irving, TX / Basking Ridge, NJ (Hybrid Job)
Contract Dur:1+ Year Contract

Job Description:
Need is for someone who is technically adept at both API and platform management and can drive AI use cases in the SDLC process across multiple teams.
Strong experience in using AI.
Someone good with Java, React, NSA stack technology.
Someone who understand the potential use cases they can build to drive AI adoption.
Good experience with APIs in the platform.
Conceptualize, design, architect, and develop Spring boot reactive microservices with spring webflux
Knowledge of service oriented multi-tier applications.
Conceptualize, design, architect & develop AI agent frameworks and systems, including defining agent capabilities, interaction flows, and decision-making logic.
Select appropriate AI models (LLMs, machine learning models, etc.), tools, and technologies for agent development.
Design and implement agent memory systems, knowledge bases, and context management mechanisms.
Develop, code, and test AI agents using programming languages such as Python, and relevant AI/ML libraries and frameworks (e.g., LangChain, LlamaIndex, TensorFlow, PyTorch, scikit-learn).
Integrate agents with various data sources, APIs, and external systems.
Implement Natural language Understanding (NLU) and natural language generation (NLG) components for human-like interaction.
Develop and fine-tune LLM prompts and orchestrate LLM interactions within agent workflows.
Develop and utilize tools for agent creation, management, and orchestration.
Implement mechanisms for agents to use external tools and APIs to augment their capabilities (e.g., web search, code execution, database queries).
Design and implement robust testing and evaluation frameworks for AI agents, focusing on performance, accuracy, reliability, and safety.
Monitor agent performance in production, identify areas for improvement, and implement optimizations.
Conduct A/B testing and other experiments to refine agent behavior and effectiveness.
Deploy AI agents to scalable and reliable infrastructure (cloud platforms like AWS, Google Cloud Platform, Azure, or on-premise).
Ensure agents adhere to security best practices and data privacy regulations.

Technical Skills:
Strong proficiency in Java 17 & above, Spring WebFlux, Python
Solid understanding of machine learning concepts, algorithms, and best practices.
Hands-on experience with Large Language Models (LLMs), including prompt engineering, fine-tuning, and integration (e.g., OpenAI GPT series, Anthropic Claude, Google Gemini, open-source models).
Experience with AI agent frameworks and libraries (e.g., LangChain, LlamaIndex, AutoGen, Microsoft Semantic Kernel).
Proficiency in developing and consuming APIs (REST, GraphQL).
Experience with database technologies (SQL, NoSQL).
Familiarity with cloud platforms (AWS, Google Cloud Platform, Azure) and containerization technologies (Docker, Kubernetes).
Understanding of software engineering principles, including data structures, algorithms, version control (Git), testing, and agile methodologies.
Knowledge of Natural Language Processing (NLP) techniques and libraries (e.g., spaCy, NLTK, Hugging Face Transformers).
Experience with REST services with various methods like GET, POST, PUT using JSON structures.

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