Custom Software Engineer

Accenture Federal Services • Herndon, VA

23h

About The Position

At Accenture Federal Services, the primary goal is to empower the US federal government to enhance national strength and safety, and improve the lives of its citizens. The organization comprises over 13,000 professionals dedicated to applying technology and ingenuity for clients across various sectors including defense, national security, public safety, civilian, and military health. Accenture Federal Services operates as a technology company within the broader global Accenture network, recognized as a Glassdoor Top 100 Best Place to Work. It fosters a collaborative and supportive environment where individuals are encouraged to belong, grow, learn, and thrive through practical experience, certifications, and industry-specific training. The role is focused on initiating positive and lasting change to advance critical missions and government operations.

Requirements

• 4 to 7+ years of professional experience in one or more of the following areas: Python, with knowledge of the libraries and frameworks that make up the Python ecosystem.
• Designing and implementing RESTful APIs using tools like FastAPI.
• Asynchronous task processing tools like Celery and RabbitMQ.
• Strong working knowledge of Docker and Kubernetes for building and deploying scalable, cloud-native applications.
• Familiarity with distributed data orchestration and processing pipelines, using tools like Spark and Airflow.
• Relational databases like PostgreSQL.
• CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and/or deploying applications within the Linux ecosystem.
• Optimizing resources across cloud deployments for cost and performance benefit.
• Active TS/SCI with Poly clearance is required

Responsibilities

• Contribute to the design and development of robust, scalable, and secure backend systems and event-driven APIs using FastAPI.
• Participate in code and technical design reviews to ensure high standards for quality, performance, and security are met.
• Collaborate with data scientists and ML engineers to integrate, containerize, and deploy AI/ML models (e.g., NLP, recommendation engines, generative AI) into production environments.
• Engineer containerized applications for deployment on cloud platforms using Kubernetes.
• Design for scale using asynchronous processing and task queues (such as Celery, RabbitMQ, Kafka) to handle long running or unreliable tasks independently from the main API.

What This Job Offers

• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 1,001-5,000 employees