AI/ML Engineer with Data Scientist - Reston, VA - W2 - 6+ Months Contract

AI/ML Engineer with Data Scientist

Reston, VA

6+ Months Contract

Top Skills' Details

1. 8-12 years of experience working as a prompt engineer/ python/ML Engineer with strong Python, SQL and AWS - experience with modeling applications
2. Prompt Engineering experience - ability to write custom code and write prompts
3. LLM's - AWS Bedrock, standard AWS services (if they dont have bedrock, thats okay -- just show me strong ML/AI, LLM candidates)

Secondary Skills - Nice to Haves

• Data analysis
• Business analysis
• LLM

Job Description

Models are being audited in Fannie Mae , this team will do the audit testing with different models to see if the model is doing what it is supposed to do, any gaps.
These are all the models Fannie Mae uses across the enterprise that are being audited.

This is a combination of a data analysts with experience in prompt engineering.
Prompt engineering, writing code, data analysis - write queries
Looking for someone with strong Python, SQL, AWS - ability to understand financial models
Any experience with LLMs - they are using Anthropic and AWS Bedrock

Experience:
8+ years overall in Software Engineering disciplines, preferably in the financial services industry
2-3 years of experience in AI/ML engineering roles
Strong programming skills in Python, SQL and experience with AWS.

Key Responsibilities:
Design, test, and refine prompts for large language models (LLMs) to support financial reporting, summarization, and client communication tools.
Analyze structured and unstructured financial data using Python and SQL, delivering insights through dashboards and reports.
Develop and maintain data pipelines and ETL workflows to support GenAI model training and evaluation.
Use AWS SageMaker to build, train, and deploy machine learning and GenAI models.
Collaborate with data scientists, analysts, and business stakeholders to align AI solutions with financial objectives.
Monitor model performance and iterate on prompt and model design to improve accuracy and relevance.
Document workflows, models, and prompt strategies for internal knowledge sharing and compliance.

Required Qualifications:
2 3 years of experience in data analysis or machine learning roles.
Proficiency in Python and SQL for data manipulation and analysis.
Hands-on experience with major AWS services, particularly SageMaker, S3, Redshift, and Lambda.
Experience working with LLMs (Anthropic Claude, Sonnet) and prompt engineering techniques.
Strong understanding of financial data, KPIs, and reporting standards.
Excellent communication and collaboration skills.