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Entry Level Retrieval Augmented Generation Jobs in Boston, MA

Nastech Global

GCP Gen AI Architect

Boston, MA · On-site

Build Retrieval-Augmented Generation (RAG) pipelines to improve the quality of AI-generated responses. Collaborate with various stakeholders to integrate and deploy AI models into production ...

SolGenie

Full Stack UI Developer

Boston, MA · On-site

Optimize LLM API interactions using prompt engineering, retrieval-augmented generation (RAG), contex management, and performance tuning. * Code Quality / Code Reviews: Write clean, maintainable, and ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$35 - $45/hr

Experience in RAG (Retrieval-Augmented Generation) implementations. * Knowledge of MLOps tools and CI/CD pipelines. * Experience with Databricks and Apache Spark. Technical Skills * Python * SQL

Winaxis

AI/ML Engineer

Boston, MA · On-site

$124K - $149K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$32 - $35/hr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$30 - $35/hr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

Holland & Knight

AI Legal Engineer

Boston, MA · On-site

Demonstrated ability to design and build AI-enabled workflows in legal or professional-services settings, including prompt engineering, retrieval-augmented generation (RAG) concepts, and evaluation ...

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Entry Level Retrieval Augmented Generation information

What are entry level retrieval augmented generation jobs?

Entry level retrieval augmented generation jobs involve assisting in the development and optimization of AI systems that combine information retrieval techniques with generative models. Employees in these roles typically help build, test, and maintain systems where AI retrieves relevant data from large databases to enhance the accuracy and relevance of generated responses. These positions often require basic skills in programming, machine learning, and familiarity with natural language processing. They are ideal for recent graduates or those new to AI, offering opportunities to learn about modern AI architectures and contribute to innovative projects. Entry level workers may work under the guidance of senior engineers or researchers, supporting experimentation and evaluation tasks.

What are the key skills and qualifications needed to thrive as an Entry Level Retrieval Augmented Generation Specialist, and why are they important?

To thrive as an Entry Level Retrieval Augmented Generation Specialist, you need a foundational understanding of natural language processing (NLP), information retrieval, and basic programming skills, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, vector databases (like FAISS or Pinecone), and frameworks for large language models (LLMs) is typically required. Strong problem-solving abilities, attention to detail, and effective communication help you collaborate and troubleshoot solutions in team environments. These skills and qualities are crucial for building reliable RAG systems that deliver accurate and relevant information to users.

What is the difference between Entry Level Retrieval Augmented Generation vs Entry Level Data Scientist?

AspectEntry Level Retrieval Augmented GenerationEntry Level Data Scientist
Required CredentialsBasic programming, understanding of NLP and AI conceptsBachelor's in Data Science, Computer Science, or related field
Work EnvironmentTech companies, AI startups, research labsTech firms, finance, healthcare, consulting
Industry UsageAI development, NLP applications, chatbot creationData analysis, predictive modeling, data-driven decision making

Entry Level Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with generative AI, requiring knowledge of NLP and programming. Entry Level Data Scientist involves analyzing data, building models, and deriving insights, often with a broader data analysis skill set. While both roles require technical skills, Retrieval Augmented Generation is more specialized in AI model development, whereas Data Scientists work across various data projects.

What are some common challenges faced by entry-level professionals working in Retrieval Augmented Generation (RAG) roles?

Entry-level professionals in Retrieval Augmented Generation (RAG) often encounter challenges such as understanding how to effectively combine information retrieval systems with large language models and adapting to rapidly evolving technologies. Balancing accuracy and efficiency when designing or fine-tuning retrieval pipelines can also be a learning curve. Additionally, you may need to collaborate closely with data engineers, machine learning specialists, and product teams to ensure the RAG system aligns with business requirements. Staying proactive in learning and engaging with peers can help overcome these challenges and accelerate career growth.
What are popular job titles related to Entry Level Retrieval Augmented Generation jobs in Boston, MA? For Entry Level Retrieval Augmented Generation jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Entry Level Retrieval Augmented Generation jobs in Boston, MA look for? The top searched job categories for Entry Level Retrieval Augmented Generation jobs in Boston, MA are:
Entry Level Retrieval Augmented Generation jobs near you
Infographic showing various Entry Level Retrieval Augmented Generation job openings in Boston, MA as of July 2026, with employment types broken down into 62% Full Time, 14% Part Time, 11% Temporary, and 13% Contract. Highlights an 100% In-person job distribution.
Senior AI Engineer - Boston, MA - Contract Opportunity

Senior AI Engineer - Boston, MA - Contract Opportunity

Zodiac Solutions

Boston, MA • On-site

$113K - $155K/yr

Contractor

Posted 17 days ago

Job description

Job Title: Senior AI Engineer
Location: Boston, MA – 4 days/week onsite

Duration: Contract Opportunity

Senior Level – 15+ yrs Only

F2F Interview is required for final round mandatory.

Job Description:

This role focuses on developing AI applications powered by large language models (LLMs), retrieval-augmented generation (RAG), Model Context Protocol (MCP) servers, and Agentic AI across the enterprise. Need someone with Langchain/LangGraph exp.

Seeking a highly skilled AI Engineer to design and build Generative and Agentic AI systems that transform how our company operates and serves customers. This role focuses on developing AI applications powered by large language models (LLMs), retrieval-augmented generation (RAG), Model Context Protocol (MCP) servers, and Agentic AI across the enterprise for internal and customer facing use cases.

The ideal candidate has strong experience with a modern AI/ML stack, including a profound Python experience, including AI relevant packages and tools, LLM architectures and frameworks, RAG, MCP, prompt engineering, use of AI tools, budling teams of AI agents, and production-grade AI systems design and development.

You will work closely with technology, data, and business teams to build scalable AI solutions that drive operational efficiency and innovation across the enterprise.

Key Responsibilities:

  • Design, build, and deploy scalable LLM-powered applications, solutions, and digital products for customer support, business use cases, and internal productivity.
  • Develop AI agents, Agentic Platforms, and Agentic AI systems capable of reasoning, planning, and executing multi-step workflows.
  • Implement Retrieval-Augmented Generation (RAG) architectures and pipelines to leverage proprietary data, and internal knowledge bases.
  • Build MCP servers, multi-connectivity enabled and multi-modal Agentic platforms, and AI assistants to support internal teams and customer facing.
  • Integrate AI/ML solutions with enterprise systems, APIs, and data platforms.
  • Design prompt strategies, evaluation frameworks, and guardrails to ensure accuracy, security, and regulatory compliance.
  • Optimize LLM performance through fine-tuning, prompt engineering, and model orchestration.
  • Develop MLOps and LLMOps pipelines for monitoring, evaluation, and continuous improvement of AI systems.
  • Help in forming buy or build decisions and work with external vendors, consultants and internal teams to deliver scalable top-quality AI solutions timely.
  • Stay up to date with emerging advancements in AI/ML, Generative AI, Agentinc frameworks, and model architectures. 

Required Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or related field.
  • 2+ years of software engineering experience, including work with machine learning (ML) or AI systems.
  • Hands-on experience building LLM-powered applications in production environments.
  • Strong programming skills in Python, and familiarity with AI/ML relevant packages such as NumPy, Pandas, SciPy and Scikit-learn.
  • Experience with LLM ecosystems such as:
    • OpenAI / Anthropic / Google, Open-source LLMs
    • Hugging Face
    • LangChain, LlamaIndex, or similar orchestration frameworks
  • Experience building RAG pipelines and working with vector databases.
  • Understanding of prompt engineering, embeddings, and model evaluation.
  • Experience building APIs, MCPs, and scalable backend services.
  • Experience with Claude Code, Codex, Cursor, GitHub Copilot, or similar.
  • Ability to collaborate with cross-functional teams.
  • Passion for technology and AI.

Preferred Qualifications:

  • Experience building AI agents, agentic platforms, or autonomous workflows.
  • Familiarity with agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, Frontier, etc.).
  • Experience with fine-tuning LLMs or parameter-efficient training methods (LoRA, PEFT).
  • Experience with cloud-based AI infrastructure (AWS, Azure, or GCP).
  • Familiarity with Java, JS, and Java applications and environments.
  • Experience with MLOps / LLMOps tools and evaluation pipelines.

Experience implementing AI governance, observability, and guardrails.