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Generative Ai Jobs in Boston, MA (NOW HIRING)

Own the Generative AI product vision and roadmap, with primary focus on Microsoft Copilot across M365 and Power Platform along with developments in SAP Joule * Define and track measurable outcomes ...

Generative AI Development: Develop and deploy generative AI models and large language models (LLMs) for multimodal document processing, focusing on extracting structured data from technical drawings ...

Xometry is seeking an exceptional Principal Data Scientist to join our Generative AI team. The ideal candidate will have a passion for advancing machine learning and generative AI capabilities ...

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Generative Ai information

What is a generative AI?

A Generative AI job involves developing, fine-tuning, or deploying AI models that can create content such as text, images, music, or code. Professionals in this field work with machine learning frameworks, large language models, and neural networks to improve AI-generated outputs. Roles may include AI researchers, machine learning engineers, prompt engineers, or data scientists specializing in generative models. These jobs require expertise in programming (Python, TensorFlow, PyTorch), data processing, and AI ethics.

What does a generative AI specialist do?

A Generative AI Specialist typically focuses on designing, training, and fine-tuning generative models, such as those used for text, image, or audio generation. You may be responsible for researching the latest advancements, preparing datasets, evaluating model performance, and collaborating closely with data engineers, product managers, and software developers to integrate solutions into products. In many organizations, you’ll also participate in brainstorming sessions to explore new applications of generative AI, contribute to technical documentation, and support model monitoring or improvement post-deployment. This role requires a blend of technical expertise and teamwork, offering a dynamic environment where you can have a direct impact on cutting-edge innovation.

What are the key skills and qualifications needed to thrive in the generative AI position?

To thrive as a Generative AI Specialist, you need a strong background in machine learning, deep learning, and natural language processing, often supported by a relevant degree such as computer science or data science. Expertise with frameworks like TensorFlow or PyTorch, proficiency in Python, and knowledge of cloud platforms are commonly expected, with additional certifications in AI or related fields seen as valuable assets. Strong problem-solving abilities, creativity, and effective communication skills set top candidates apart. These skills are crucial for designing, developing, and deploying advanced generative models that address real-world business needs.

Is generative AI a career?

Generative AI is a growing field with roles such as AI engineer, research scientist, and data scientist focused on developing and improving AI models. Careers in this area typically require skills in machine learning, programming, and data analysis, and often involve working with tools like neural networks and deep learning frameworks.

What are the most commonly searched types of Generative Ai jobs in Boston, MA?

The most popular types of Generative Ai jobs in Boston, MA are:

What cities near Boston, MA are hiring for Generative Ai jobs?

Cities near Boston, MA with the most Generative Ai job openings:

Infographic showing various Generative Ai job openings in Boston, MA as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 69% Physical, 4% Hybrid, and 27% Remote job distribution.

Agentic AI & Generative AI Engineer

Career Soft Solutions Inc

Boston, MA • On-site

$105K - $145K/yr

Other

This job post has expired today. Applications are no longer accepted.


Job description

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.