Accenture Federal Services
US federal government
CustomSoftwareEngineeringLead
Neural analysis suggests this role is
optimal for Lead candidates.
“Custom Software Engineering Lead at Accenture Federal Services. Skills: Python, FastAPI, Docker, Kubernetes, Cloud Platforms (AWS, GCP, Azure), CI/CD, AI/ML Model Deployment, Distributed Systems, Big Data Technologies. Lead the design and development of robust, scalable, and secure backend systems and event-driven APIs. Define the technical direction and system architecture”
What You'll Achieve.
Make the nation stronger and safer; Make life better for people; Move missions and the government forward; Ensure systems are observable and resilient in production environments
Industry & Context.
Active TS/SCI with Poly security clearance, Work authorization that does not now or in the future require sponsorship of a visa
What They're Looking For.
Must Have
7+ years of professional experience, Proficient in Python, Experience designing and implementing RESTful APIs using tools like FastAPI, working knowledge of Docker and Kubernetes for building and deploying scalable, cloud-native applications, 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
Nice to Have
Familiarity with UI/UX frameworks used with Python applications, Familiarity with distributed data orchestration and processing pipelines, using tools like Spark and Airflow, Experience with asynchronous task processing tools like Celery and RabbitMQ
What You'll Do.
Lead the design and development of robust
and secure backend systems and event-driven APIs
Define the technical direction and system architecture
Serve as a primary contributor to the codebase
Conduct thorough code and technical design reviews
Collaborate with data scientists and ML engineers to integrate
and deploy AI/ML models
Engineer containerized applications for deployment on cloud platforms
Design for scale using asynchronous processing and task queues
Manage big data processing workflows and the storage of that data
Implement best practices for testing
How You'll Work.
Team & Collaboration
Collaborate with data scientists and ML engineers; Mentor and guide a team of engineers; Fostering a culture of continuous learning, collaboration, and technical excellence
Full Job Description
At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer and life better for people. Our 13,000+ people are united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. Join Accenture Federal Services, a technology company within global Accenture. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and caring community where you feel like you belong and are empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more. Join us to drive positive, lasting change that moves missions and the government forward! Job Description: Technical Leadership & Architecture: 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. Hands-on Development & Code Quality: 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. Deploy AI Models: 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. Containerization & Orchestration: Engineer containerized applications for deployment on cloud platforms using Kubernetes. Asynchronous Processing & Distributed Systems: 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. Big Data Search & Storage: Manage big data processing workflows and the storage of that data in obje
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