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Python Developer - AI, AWS, Sagemaker - ZL

Job in Reston, Fairfax County, Virginia, 22090, USA
Listing for: TryApplyNow
Full Time position
Listed on 2026-06-21
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 100000 - 135000 USD Yearly USD 100000.00 135000.00 YEAR
Job Description & How to Apply Below
# Python Developer - AI, AWS, Sagemaker - ZLSESFull Time junior Hybrid Reston , Virginia, USPosted Yesterday##

Role Overview SES is hiring a entry-level Python Developer - AI, AWS, Sagemaker - ZL. This is a full-time hybrid role, based in Reston. Part of SES's Qa hiring, posted yesterday. Full responsibilities, required qualifications, and the apply link are listed in the description below.## Salary Context Salary is not disclosed in this posting. Market median for Junior-level Qa roles is $100k-$135k (based on 63 comparable listings).

Many employers share specifics during the interview process or after an initial screen.## Resume Keywords to Include Make sure these keywords appear in your resume to improve ATS scoring

PythonSQLAWSBenefits

ComplianceAIDeveloper Type Sign  up free to auto-tailor your resume with all these keywords and get a higher ATS score##

Job Description Python/AI Developer Type: W2 With Benefits - No C2C

Location:

Hybrid 2-3 days onsite in Reston, VA Technical

Skills:

* Python* Gen AI
* SQL* AWS Data Services
* LLM* ML
* Sagemaker

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 Sage Maker 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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