Associate Software Developer

Venerable•West Chester, PA

1d•Hybrid

About The Position

This hybrid role is a member of a multidisciplinary Agile team responsible for developing, deploying, and stewarding mission-critical data systems, micro-services, web applications, and AI/ML solutions. The incumbent supports the full Application Lifecycle Management (analysis, design, development, validation, deployment, support) and collaborates with senior engineers and business stakeholders to deliver effective software and AI solutions. The role also involves supporting the development and deployment of machine learning models, primarily using AWS services. Principle Responsibilities: Assist in designing, developing, and deploying machine learning models using AWS services (SageMaker, Lambda, EC2, S3, Redshift). Support automated workflows for model training and deployment (AWS Step Functions, Lambda). Monitor model performance and suggest adjustments. Work across the full ALM lifecycle on system elements (clients, servers, services, middleware, persistence, communication). Participate in design reviews, code reviews, and practice reviews. Ensure technical approaches align with architectural blueprints and approved practices. Manage and preprocess datasets using AWS data services. Engage in software configuration management, CI/CD, automated testing, and DevSecOps practices. Collaborate with data scientists, software engineers, and business stakeholders to understand requirements and deliver solutions. Maintain documentation of models, processes, workflows, and technical approaches. Participate in Level 2+ support for team products and systems. Engage in ongoing operational and practice improvements. Follow best practices for data privacy and protection, ensuring compliance with organizational security policies.

Requirements

Effective communication and documentation skills.
Strong analytical and problem-solving capabilities.
Agile mindset and ability to adapt in a fast-paced environment.
Ability to understand the big picture and focus on critical-path work.
Experience in insurance, financial services, or other highly regulated industries is highly desirable.
Bachelor’s degree in Computer Science, Data Science, IT, or related field (preferred).
1–3+ years professional experience in AI/ML and enterprise-class SOA/micro-service systems and web-based applications.
Experience with AWS services: SageMaker, Lambda, EC2, S3, EFS, SNS, SQS, EventBridge, Kinesis, Aurora, DynamoDB, RDS Proxy, Glue, Athena, Redshift/Spectrum.
Familiarity with Azure “Cloud Native” solutions concepts is a plus.
Basic proficiency in Python and machine learning libraries (TensorFlow, PyTorch, scikit-learn).
Proficiency with C# and at least one of: Python, PowerShell, SQL, JavaScript.
Understanding of OOA/OOD, system modeling (UML preferred), and distributed systems patterns (SOA/micro-services, CQRS, event-stream processing, pub/sub messaging).
Experience with containerization (Docker), version control (Git/GitHub), CI/CD tooling (Jenkins, Bamboo, TeamCity, AWS CodePipeline).
Experience with automated BDD test tooling (SpecFlow, Cucumber).
Familiarity with messaging serialization (Protobufs, Thrift, Avro, JSON), RDMS persistence (ODBC/SQL), NoSQL repositories, MoM platforms (MQ, JMS, Kafka), JavaScript browser UI frameworks (Angular, React).

Nice To Haves

AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect (Associate level).
Experience with NLP, computer vision, reinforcement learning, and generative models.
Experience with code profiling, optimization, and analysis tooling is a plus.
Familiarity with Kanban, Lean, or XP practices is a plus.

Responsibilities

Assist in designing, developing, and deploying machine learning models using AWS services (SageMaker, Lambda, EC2, S3, Redshift).
Support automated workflows for model training and deployment (AWS Step Functions, Lambda).
Monitor model performance and suggest adjustments.
Work across the full ALM lifecycle on system elements (clients, servers, services, middleware, persistence, communication).
Participate in design reviews, code reviews, and practice reviews.
Ensure technical approaches align with architectural blueprints and approved practices.
Manage and preprocess datasets using AWS data services.
Engage in software configuration management, CI/CD, automated testing, and DevSecOps practices.
Collaborate with data scientists, software engineers, and business stakeholders to understand requirements and deliver solutions.
Maintain documentation of models, processes, workflows, and technical approaches.
Participate in Level 2+ support for team products and systems.
Engage in ongoing operational and practice improvements.
Follow best practices for data privacy and protection, ensuring compliance with organizational security policies.