Overview
Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)
Skills
Gen AI
Python
SQL
Job Details
Mid to Senior Level GenAI developer.
Location: Reston, Virginia (3 Days Hybrid Role)
Location: Reston, Virginia (3 Days Hybrid Role)
Duration-12 months
Note- 2nd Round will be in person Interview
Experience:
8+ years overall in Software Engineering disciplines, preferably in the contracts and legal domain.
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.
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.
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