AI Developer

Berkley•Manassas, VA

About The Position

The AI Developer is an entry-level engineering role focused on learning and contributing to the development, testing, and operation of AI-enabled features and services. Under the guidance of experienced engineers, the role builds Python components, supports retrieval-augmented generation (RAG) and agentic workflows, integrates AI capabilities into applications, and applies sound software engineering, data, security, and Responsible AI practices. It emphasizes technical growth, hands-on delivery, collaboration, and continuous improvement. Berkley Technology Services expects team members to actively incorporate AI tools into their daily work as a standard part of modern engineering. The role collaborates with engineers, analysts, QA, architects, and product owners, and participates in agile ceremonies, demonstrations, peer reviews, and feedback cycles. Travel is approximately 5–10%.

Requirements

0–4 years of experience with Python and familiarity with data structures, APIs, debugging, and automated testing.
Understanding of machine learning, generative AI, large language models, prompt design, and common AI application patterns.
Demonstrated comfort using AI-assisted tools in a professional context and treating AI as a standard part of modern workflow.
Understanding of data preparation, structured and unstructured data, metadata, data quality, and basic data modeling concepts.
Familiarity, through coursework, internships, projects, or practical experience, with embeddings, vector search, and retrieval-augmented generation.
Exposure to Azure AI Foundry, AWS Bedrock, GCP Vertex AI, or similar cloud AI platforms.
Exposure to agent or workflow frameworks such as LangGraph, n8n, Agent Development Kit, or similar.
Familiarity with Git, CI/CD concepts, containers, and cloud development environments.
Basic understanding of REST APIs and integrating backend services with web applications.
Experience with AI observability in platforms such as Datadog and Langsmith.
Travel of approximately 5–10%.

Nice To Haves

Professional experience in software engineering, data engineering, AI/ML engineering, or a related technical field; relevant internships, academic projects, open-source contributions, and personal projects may also be considered.
Ability to write readable Python code and apply fundamental software engineering practices, including version control, testing, documentation, and code review.
Foundational understanding of how data is prepared, governed, and evaluated for use in AI applications.
Ability to learn new AI frameworks, cloud services, and engineering tools with coaching and documentation.
Analytical problem-solving skills and the ability to troubleshoot issues systematically and communicate findings clearly.
Ability to work collaboratively with technical and business stakeholders, accept feedback, and deliver incremental improvements.
Commitment to secure engineering, Responsible AI, customer service, and continuous learning.

Responsibilities

Contribute to Python services, APIs, workers, and reusable components that expose AI capabilities, following established coding, testing, and review standards.
Assist with RAG pipelines, including data ingestion, document parsing, chunking, embeddings, indexing, retrieval, and result validation.
Configure and enhance agentic workflows using approved frameworks; support prompt and workflow iteration and execute defined evaluations.
Prepare and validate structured and unstructured data for AI use cases; help identify data quality, metadata, access, and lineage issues.
Integrate AI capabilities into web applications and services with guidance, including input/output validation and error handling.
Use source control, CI/CD pipelines, containers, and cloud development environments in accordance with team standards.
Add and monitor application logs, metrics, and traces; assist with troubleshooting, defect resolution, and production support.
Apply Responsible AI, data protection, secure coding, and access-control practices; implement established guardrails and PII-handling patterns.
Create and maintain technical documentation, test evidence, runbooks, and user-facing guidance.
Collaborate with engineers, analysts, QA, architects, and product owners; participate in agile ceremonies, demonstrations, peer reviews, and feedback cycles.