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Entry Level Retrieval Augmented Generation Jobs in Washington, DC

Experience with Retrieval-Augmented Generation * Knowledge of AI operations and MLOps practices for deploying and maintaining machine learning models. * Familiarity with secure coding principles and ...

LLM-enabled backend services** using structured prompting, tool/function calling, and retrieval-augmented generation (RAG). * Design and implement **agentic workflows** (multi-step reasoning, tool ...

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Entry Level Retrieval Augmented Generation information

What is an entry level retrieval augmented generation job?

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?

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 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 the most commonly searched types of Retrieval Augmented Generation jobs in Washington, DC? The most popular types of Retrieval Augmented Generation jobs in Washington, DC are:
What job categories do people searching Entry Level Retrieval Augmented Generation jobs in Washington, DC look for? The top searched job categories for Entry Level Retrieval Augmented Generation jobs in Washington, DC are:
Infographic showing various Entry Level Retrieval Augmented Generation job openings in Washington, DC as of August 2026, with employment types broken down into 71% Full Time, 26% Part Time, and 3% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution.

Software Engineer | Secure AI

Bestgate Engineering

Columbia, MD

Full-time

Re-posted 13 days ago


Job description

THE OPPORTUNITY

Bestgate Engineering is hiring for multiple software engineering opportunity at the intersection of backend engineering and applied AI. The role is not about training foundation models; it is about building the secure "paved road" that lets enterprise systems use AI safely, repeatably, and with the right guardrails.

WHAT YOU WILL DO
  • Design and build internal APIs, middleware, routing layers, and reusable services for secure AI integration.
  • Develop Model Context Protocol (MCP) servers and reusable AI skills that connect models to databases, microservices, and external APIs.
  • Implement enterprise guardrails for privacy, sensitive data handling, prompt injection defense, and output validation.
  • Build and test software patterns for non-deterministic AI outputs and agent-like workflows.
  • Work across backend services, applied AI patterns, and secure software delivery practices.
WHAT MAKES YOU COMPETITIVE
  • U.S. citizenship and ability to obtain a U.S. Government security clearance.
  • Strong Java software engineering experience, including modern frameworks and libraries such as Spring or Guava.
  • Experience designing enterprise APIs, RESTful services, and microservice architectures.
  • Familiarity with applied AI concepts such as prompt engineering, tool use, function calling, and Retrieval-Augmented Generation.
  • Comfort with Git, Nexus, Maven, Linux command line workflows, and agile delivery.
  • Strong troubleshooting, testing, and communication skills.
NICE TO HAVE
  • Spring AI, LangChain4j, LangChain, LlamaIndex, vector databases, semantic caching, or AI observability tools.
  • NiFi, Kafka, AWS, Kubernetes, SQL/NoSQL databases, Python scripting, or Big Data platform exposure.
  • DoD 8140/8570 certification readiness if required by the customer.
BEST FIT

Best fit for a backend engineer who wants to make AI usable in real enterprise systems without compromising security or engineering discipline.