PGMTEK Inc.
Job Description
Our client in NYC is seeking a senior software engineer to enhance their Python/Django REST microservices platform for a modern Student Information System. In addition to core development, this engineer 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 enhance 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 privacy The experienced senior software engineer will help build a modern system. You will also create 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.
You'll work hands-on writing code, designing data schemas, and deploying both traditional and ML components. You'll 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, 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, 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 road-mapping. MINIMUM QUALIFICATIONS Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent hands-on experience. 5+ years software development experience, including: Python and Django (or similar frameworks), REST APIs, Angular/React (or similar). SQL and object-relational mapping; designing and tuning relational databases. AWS cloud services: EC2, S3, Lambda, RDS, ElasticSearch, SQS/SNS. Git, CI/CD pipelines (Jenkins, CodePipeline), containerization (Docker) and orchestration (Kubernetes). 1-2 years' 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. PREFERRED QUALIFICATIONS 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.
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 enhance 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 privacy The experienced senior software engineer will help build a modern system. You will also create 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.
You'll work hands-on writing code, designing data schemas, and deploying both traditional and ML components. You'll 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, 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, 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 road-mapping. MINIMUM QUALIFICATIONS Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent hands-on experience. 5+ years software development experience, including: Python and Django (or similar frameworks), REST APIs, Angular/React (or similar). SQL and object-relational mapping; designing and tuning relational databases. AWS cloud services: EC2, S3, Lambda, RDS, ElasticSearch, SQS/SNS. Git, CI/CD pipelines (Jenkins, CodePipeline), containerization (Docker) and orchestration (Kubernetes). 1-2 years' 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. PREFERRED QUALIFICATIONS 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.