AWS Python Developer

Python, SQL, REST APIs, Data Structures & Algorithms, AWS/AZURE/GCP, Docker, Kubernetes, CI/CD, Generative AI/LLM, RAG (Retrieval-Augmented Generation) Disclaimer: We respect your online privacy. If you would like to be removed from our mailing list please reply with “Remove” in the subject and we will comply immediately. We apologize for any inconvenience caused. Please let us know if you have more than one domain. The material in this e-mail is intended only for the use of the individual to whom it is addressed and may contain information that is confidential, privileged, and exempt from disclosure under applicable law. If you are not the intended recipient, be advised that the unauthorized use, disclosure, copying, distribution, or the taking of any action in reliance on this information is strictly prohibited. We are an equal opportunity employer with a diverse workforce. Note : Any resume submitted by Synchrony Systems Inc is presented with the understanding that the candidate is being considered for your direct end-client (end-client is the company where the work will be performed). If there is any other company involved between the end-client and your company, please do not submit this resume without our written approval. If you submit the resume to another third party, Synchrony Systems Inc reserves the right to work with the third party directly.

Programming & Software Engineering: Expertise in Python (and optionally Java, JavaScript, or C#), unit testing, and version control (Git). AI/ML Knowledge: Solid grasp of machine learning and AI concepts, model behavior, and experience with NLP or chatbots. Prompt Engineering: Experience crafting and iterating prompts, including few-shot examples and output formatting techniques. RAG and Data Handling: Familiarity with embedding models, vector databases, and unstructured data processing. API and Integration Skills: Building and consuming RESTful APIs, microservices, and handling JSON/XML data formats. Data Structures & Algorithms: Knowledge of lists, dictionaries, trees/graphs, and their application in efficient agent design. Debugging & Problem-Solving: Strong troubleshooting abilities to distinguish between model and code issues. Agile and Collaborative Mindset: Comfortable working in sprints, collaborating across teams, and communicating technical needs. Domain Adaptability: Ability to quickly learn new industry concepts for tailored agent solutions. Attention to Detail & Quality: Consideration of edge cases, proper data handling, and thorough testing. Ethical Awareness: Recognize bias, confidentiality issues, and flag questionable requests.