Data Scientist - AI Engineer Job Bethesda area,Maryland USA,Software Development

MINIMUM QUALIFICATIONS

You must meet ALL of the minimum qualifications listed below.

Bachelor's degree from an accredited college or university in Computer Science, Data Science, Information Technology, Engineering, Mathematics, Statistics, or a closely related quantitative field.

AND

Applicants must have one (1) year of specialized experience equivalent to the next lower grade level demonstrating the following technical competencies:

Programming for AI/Data Science: Experience using modern programming languages standard to data science and AI engineering (Python required; experience with SQL, Scala, or Rust is a plus) to build data pipelines and integrate analytics applications.

Generative AI & LLM Frameworks: Experience developing, configuring, or integrating machine learning models and Large Language Model (LLM) orchestration frameworks (e.g., Lang Chain, Llama Index, Hugging Face, or commercial AI APIs like Azure OpenAI).

Enterprise Data & Tooling workflows: Experience utilizing collaborative notebooks, cloud-native platforms, or data management tools (e.g., Databricks, Jupyter, Copilot Studio, or Gemini) for experiment tracking, workflow automation, or model management.

Relationship & Graph Data Structures: Practical, hands‑on experience utilizing or configuring graph databases (e.g., Neo4j, Memgraph) or relational database architectures to map and analyze complex data connections.

DESIRABLE QUALIFICATIONS

Knowledge of the U.S. Postal Service
Experience in web app development utilizing front and back-end languages, libraries, and frameworks (e.g. HTML5/CSS, JavaScript, Typescript, jQuery, Bootstrap, Angular, Flask, Django, REST/SOAP APIs)
Experience with and an understanding of Dev Ops pipelines and familiarity with the principles of continuous integration and continuous deployment (CI/CD) practices
Practical experience with cloud platforms such as Azure (preferred), AWS, or GCP

EVALUATION FACTORS

You must have the experience, knowledge and skills as listed in EACH of the evaluation factors. Failure to demonstrate that you meet all of the evaluation factor requirements as listed below will result in a score of zero (0); an ineligible status, and you will not be referred for further consideration. Include your major accomplishments relevant to the position requirements in your resume.

Business Requirements & ML Translation: Ability to capture complex business requirements and adeptly transform raw organizational data into production‑ready machine learning solutions, visualizations, interactive dashboards, and executive presentations.
LLM Orchestration & RAG Frameworks: Skill in building and scaling Retrieval‑Augmented Generation (RAG) architectures using orchestration frameworks (e.g., Lang Chain, Llama Index) and vector databases to safely connect commercial or open‑source LLMs to enterprise data sources.
Model Selection & API Integration: Knowledge of commercial AI APIs (e.g., Azure OpenAI) and open‑source foundation models (e.g., Llama, Mistral), with the ability to evaluate model trade‑offs regarding context window constraints, latency, cost, and hosting infrastructure.
Advanced Programming & Core Tooling: Mastery of Python programming and analytical toolsets (e.g., Databricks, Neo4j, Power BI) to manage data engineering pipelines, implement advanced prompt engineering techniques, and enable function calling/agentic workflows.
LLM Evaluation, Guardrails, & Security: Skill in implementing validation frameworks to evaluate model outputs for accuracy, bias, and faithfulness (mitigating hallucinations), alongside deploying safety guardrails, alignment techniques, and data privacy controls.
Pipeline Automation & Scalability: Ability to design, implement, and maintain scalable, reliable, and secure…