Job Location : Remote
Job Title : Senior Software Engineer Consultant (Python/Django)-US EST Shift
Target Start Date : ASAP
Role Overview
The Senior Software Engineer will be focused on extending the Python/Django REST microservices platform for a modern high profile system. In addition to core development, the ideal candidate will integrate, deploy, and maintain machine-learning models (e.g. recommendation engines, predictive analytics, NLP interfaces) in AWS.
Key Technical Areas
- Core Stack : Python 3.x, Django REST Framework, SQL/RDS, Angular or React front-ends
- Cloud & DevOps : AWS (EC2, S3, Lambda, RDS, ElasticSearch, SQS/SNS, SageMaker), Docker/Kubernetes, Jenkins or CodePipeline
- AI/ML Integration : Collaborate with data scientists to productionize TensorFlow/PyTorch models, build MLOps pipelines (MLflow, Kubeflow), implement CI/CD for model retraining and monitoring
- Data & Compliance : Design data schemas and pipelines for training/inference, ensure FERPA-compliant data governance and Description :
USEReady seeks an experienced senior software engineer to join the development team (onshore and offshore) building a modern critical system. You will help deliver new functionality, migrate legacy data, and integrate services, all while embedding AI/ML-powered features (predictive analytics, recommendation engines, and NLP interfaces) into the Python/Django REST microservices architecture on AWS.Work hands-on writing code, designing data schemas, and deploying both traditional and ML components. Partner closely with data scientists, infrastructure, security, and project management to ensure robust, scalable, and secure services that improve student and administrator experiences.
Key Responsibilities
Development & Architecture :
- Implement and maintain Python/Django REST services and Angular/React front-ends.
- Design and optimize SQL database schemas; build data pipelines for ML training and inference.
- Integrate machine learning models (e.g., TensorFlow, PyTorch) into microservices for features like course recommendations, retention risk scoring, and automated data validation.
Cloud & DevOps
- Build and manage AWS infrastructure : EC2, S3, Lambda, RDS, Elasticsearch, SQS/SNS, and SageMaker (or equivalent).
- Extend CI/CD pipelines (Jenkins, AWS CodePipeline, Docker/Kubernetes) to support automated model retraining, testing, and deployment.
- Monitor system and model performance; implement logging, alerting, and cost-optimization best practices.
Collaboration & Mentorship
- Partner with Data Science to translate business use cases into ML workflows (data ingestion, feature engineering, and model tuning).
- Review peers' code and mentor junior developers on Python, Django, AWS services, and MLOps practices.
- Communicate complex technical and AI/ML concepts clearly to both technical and non-technical stakeholders.
Standards & Compliance
- Enforce coding, security, and data-privacy standards (FERPA compliance) across development and deployment.
- Maintain documentation for codebases, ML pipelines, and operational runbooks.
- Participate in sprint planning, estimation, and technical EXPERIENCE :
- Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent hands-on experience.
- 5+ years of software development experience, including :
- Python and Django (or similar frameworks), REST APIs, and Angular/React (or similar).
- SQL and object-relational mapping; designing and tuning relational databases.
- AWS cloud services : EC2, S3, Lambda, RDS, Elasticsearch, and SQS/SNS.
- Git, CI/CD pipelines (Jenkins, CodePipeline), containerization (Docker), and orchestration (Kubernetes).
- 1 to 2 years of experience integrating or deploying machine learning models in production.
- Strong analytical, problem-solving, and debugging skills.
- Excellent communication, teamwork, and ability to manage shifting priorities.
Desired Experience
- Master's degree or higher in a technical field (Data Science, AI/ML, Computer Science).
- Prior work on Higher Education or Student Information Systems.
- Hands-on experience with MLOps frameworks (MLflow, Kubeflow) and cloud ML services (SageMaker, Vertex AI).
- Familiarity with NLP libraries (spaCy, Hugging Face Transformers) for chatbots or text analytics.
- Experience with data governance, privacy regulations, and ethical AI practices in educational contexts.
(ref:hirist.tech)
