Apprentice Generative Ai Engineer Jobs in Massachusetts

Job Title: Agentic AI & Generative AI Engineer
Location: Onsite – Richardson,TX/ charlotte, NC
Employment Type: Full-Time / Contract

Job Summary

We are seeking an experienced Agentic AI & Generative AI Engineer to design, develop, and deploy next-generation AI applications powered by Large Language Models (LLMs), autonomous AI agents, and modern AI frameworks. The ideal candidate will have hands-on experience building intelligent AI systems using OpenAI, Anthropic, Gemini, Llama, LangChain, LangGraph, CrewAI, AutoGen, and Retrieval-Augmented Generation (RAG) architectures.

This role involves developing AI agents capable of reasoning, planning, tool usage, memory management, and workflow automation while integrating enterprise data sources and cloud infrastructure.

Key Responsibilities

• Design, build, and deploy Agentic AI solutions capable of autonomous decision-making and multi-step reasoning.
• Develop Generative AI applications using Large Language Models (LLMs) such as GPT-4/5, Claude, Gemini, and Llama.
• Build multi-agent systems using frameworks like LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar.
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge repositories.
• Develop AI copilots, intelligent assistants, chatbots, and workflow automation solutions.
• Integrate AI applications with REST APIs, enterprise applications, databases, and cloud services.
• Fine-tune prompt engineering strategies to improve response quality, reasoning, and accuracy.
• Design agent memory, planning, orchestration, and tool-calling capabilities.
• Deploy AI workloads on Azure, AWS, or Google Cloud using containerized architectures.
• Optimize inference performance, latency, scalability, and cost.
• Implement AI governance, security, responsible AI, and compliance best practices.
• Monitor model performance and continuously improve AI systems using user feedback and evaluation metrics.
• Collaborate with product owners, architects, data scientists, and software engineers throughout the AI development lifecycle.

Required Qualifications

• Bachelor''''''''s or Master''''''''s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
• 5+ years of software engineering experience.
• 2+ years of hands-on experience building Generative AI or LLM-powered applications.
• Strong programming skills in Python.
• Experience with OpenAI, Anthropic Claude, Gemini, Llama, or other foundation models.
• Strong understanding of Prompt Engineering and LLM optimization.
• Experience building RAG applications.
• Experience with Vector Databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or Azure AI Search.
• Experience with LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
• Knowledge of embeddings, chunking, semantic search, and retrieval optimization.
• Experience integrating AI solutions with REST APIs and enterprise applications.
• Strong understanding of Docker, Kubernetes, CI/CD pipelines, and Git.
• Experience deploying AI solutions on Azure, AWS, or Google Cloud.

Preferred Qualifications

• Experience fine-tuning open-source LLMs.
• Knowledge of Model Context Protocol (MCP).
• Experience with AI agent orchestration platforms.
• Familiarity with AI observability tools such as LangSmith, Phoenix, Weights & Biases, or MLflow.
• Experience with Azure AI Foundry, Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
• Knowledge of knowledge graphs and graph databases (Neo4j).
• Experience implementing Responsible AI and AI governance frameworks.
• Experience working with structured and unstructured enterprise data.

Technical Skills

Programming

• Python
• SQL
• JavaScript (preferred)

AI/LLMs

• OpenAI GPT
• Anthropic Claude
• Google Gemini
• Meta Llama
• Mistral
• Hugging Face Transformers

Agentic AI Frameworks

• LangGraph
• CrewAI
• AutoGen
• Semantic Kernel
• OpenAI Agents SDK

RAG & Retrieval

• LangChain
• LlamaIndex
• Azure AI Search
• Pinecone
• Weaviate
• Chroma
• FAISS
• Milvus

Cloud Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform

DevOps

• Docker
• Kubernetes
• GitHub Actions
• Azure DevOps
• Jenkins
• Terraform

Databases

• PostgreSQL
• MongoDB
• Redis
• Neo4j

APIs & Integration

• REST APIs
• GraphQL
• MCP
• Webhooks

Observability

• LangSmith
• MLflow
• Weights & Biases
• OpenTelemetry

Nice-to-Have Skills

• AI workflow automation
• Multi-agent orchestration
• Human-in-the-loop systems
• Reinforcement learning concepts
• AI safety and governance
• Prompt optimization and evaluation
• Knowledge graph integration
• AI-powered business process automation

Soft Skills

• Strong analytical and problem-solving skills.