
location_onUnorganized Borough, United States
Systems Engineering Services Corporation (SESC), operating under the OpenKyber brand, was founded in 1989 as a leading provider of technology solutions to Fortune 1000 companies and government organizations. Our corporate mission is to deliver valuable solutions to client technology needs through responsive, high-quality services. We specialize in Accelerated Development Services, Architecture Services, Data Services, Testing, Cyber Security, and DevOps.
This position is designed for an experienced Software Engineer to bridge the gap between traditional data engineering and emerging Generative AI capabilities within the financial sector. You will serve as a key technical resource, leveraging Python, SQL, and AWS to build robust AI/ML solutions that drive financial reporting, summarization, and client communication.
In this role, you will not only design and refine prompts for Large Language Models (LLMs) but also architect the underlying data pipelines and ETL workflows required for model training and evaluation. Your work will directly impact how financial data is analyzed and presented, utilizing tools like AWS SageMaker to deploy machine learning models. You will collaborate closely with data scientists, analysts, and business stakeholders to ensure AI solutions align with specific financial objectives while maintaining rigorous standards for model performance and accuracy.
To discuss the details of this position, the client company environment, and next steps, please contact us directly. We invite you to reach out to initiate the conversation regarding your qualifications and interest.
For applications and inquiries, please email: hirings@openkyber.com
Work model: On-site
Unorganized Borough, United States
Skills: Python, Gen Ai, SQL, Aws, LLM, ML, Sagemaker, Ai/ml Engineering, Data Visualization, Power Bi.
Education: Bachelor's degree in Computer Science, Data Science, Finance, or a related field.
Experience in the finance or fintech industry. Familiarity with vector databases (e.g., FAISS, Pinecone) and retrieval-augmented generation (RAG). Exposure to data visualization tools (e.g., Power BI, Tableau). Understanding of MLOps practices and model lifecycle management.