Custom Software Engineering Lead

Accenture Federal Services•Chantilly, VA

About The Position

At Accenture Federal Services, our purpose is to make the US federal government stronger and safer, and to improve lives through technology and ingenuity. We are a community where you can grow, learn, and thrive. Join us to drive positive, lasting change that moves missions and the government forward!

Requirements

7+ years of professional experience in one or more of the following areas:
Proficient in Python, with knowledge of the libraries and frameworks that make up the Python ecosystem.
Familiarity with UI/UX frameworks used with Python applications.
Experience designing and implementing RESTful APIs using tools like FastAPI.
Experience with 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.
Experience configuring, deploying, and tuning distributed search technologies, such as: Trino, OpenSearch, and Elasticsearch.
Experience with relational databases like PostgreSQL.
Experience with CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and deploying applications within the Linux ecosystem.
Active TS/SCI with Poly security clearance is required.

Responsibilities

Lead the design and development of robust, scalable, and secure backend systems and event-driven APIs using FastAPI.
Define the technical direction and system architecture, balancing short-term deliverables with long-term scalability and maintainability.
Serve as a primary contributor to the codebase, leading by example through hands-on development.
Conduct thorough 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.
Manage big data processing workflows and the storage of that data in object storage, indexing tools, and distributed query engines (PySpark, OpenSearch, Hive Metastore, Trino).
Mentor and guide a team of engineers, fostering a culture of continuous learning, collaboration, and technical excellence.
Implement best practices for testing, automation, monitoring, and deployment, ensuring systems are observable and resilient in production environments.