O
Agentic AI Engineer
Oscar Technology
3 hours ago
Full-time
On-site
Atlanta, Georgia, United States
Role
We are seeking an AI Engineer to lead the development of our high-performance backend systems, data pipelines, and compute orchestration layers. You will build the core platform infrastructure that connects physics-based simulation engines and AI agent workflows with global enterprise clients. Key Responsibilities
System Architecture: Design, build, and maintain high-concurrency backend microservices in Python that power complex physical simulations and active learning loops. Data Pipelines & Integration: Build scalable ingestion and processing pipelines (RAG, Delta Lake) to unify fragmented enterprise data and physical schemas into a high-performance semantic layer. Agentic Compute Orchestration: Partner with AI/ML research teams to integrate agentic workflows, sandboxed execution systems, and model-serving infrastructure into secure, enterprise-grade cloud environments. Operational Excellence: Harden core system capabilities around multi-tenancy, security, auditability, CI/CD automation, and low-latency API performance. Qualifications
Experience: 6 years of production software engineering experience building scalable backend microservices, high-throughput distributed systems, and API architectures. Core Tech Stack: Advanced proficiency in Python, alongside modern web frameworks (FastAPI, gRPC) and relational/NoSQL databases (PostgreSQL, Redis). Infrastructure & Cloud: Strong experience with containerization, orchestration, and IaC tools including Docker, Kubernetes, and Terraform on AWS, Azure, or Google Cloud Platform. Data & Systems: Hands-on experience with asynchronous task processing (Celery, RabbitMQ), large-scale data pipelines, and high-performance system design. Education: B.S. or M.S. in Computer Science, Software Engineering, Applied Mathematics, or a related quantitative field. Preferred / Bonus Qualifications
Experience or background in CAD engines, CAE/CFD software, computational physics, or digital twin simulation platforms. Exposure to physics-informed machine learning models, active learning loops, or autonomous LLM multi-agent orchestration. Knowledge of C/C++ low-level optimization, CUDA, or parallel compute systems. Benefits
This role offers competitve pay, hybrid work options, and comprehensive health benefits.
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We are seeking an AI Engineer to lead the development of our high-performance backend systems, data pipelines, and compute orchestration layers. You will build the core platform infrastructure that connects physics-based simulation engines and AI agent workflows with global enterprise clients. Key Responsibilities
System Architecture: Design, build, and maintain high-concurrency backend microservices in Python that power complex physical simulations and active learning loops. Data Pipelines & Integration: Build scalable ingestion and processing pipelines (RAG, Delta Lake) to unify fragmented enterprise data and physical schemas into a high-performance semantic layer. Agentic Compute Orchestration: Partner with AI/ML research teams to integrate agentic workflows, sandboxed execution systems, and model-serving infrastructure into secure, enterprise-grade cloud environments. Operational Excellence: Harden core system capabilities around multi-tenancy, security, auditability, CI/CD automation, and low-latency API performance. Qualifications
Experience: 6 years of production software engineering experience building scalable backend microservices, high-throughput distributed systems, and API architectures. Core Tech Stack: Advanced proficiency in Python, alongside modern web frameworks (FastAPI, gRPC) and relational/NoSQL databases (PostgreSQL, Redis). Infrastructure & Cloud: Strong experience with containerization, orchestration, and IaC tools including Docker, Kubernetes, and Terraform on AWS, Azure, or Google Cloud Platform. Data & Systems: Hands-on experience with asynchronous task processing (Celery, RabbitMQ), large-scale data pipelines, and high-performance system design. Education: B.S. or M.S. in Computer Science, Software Engineering, Applied Mathematics, or a related quantitative field. Preferred / Bonus Qualifications
Experience or background in CAD engines, CAE/CFD software, computational physics, or digital twin simulation platforms. Exposure to physics-informed machine learning models, active learning loops, or autonomous LLM multi-agent orchestration. Knowledge of C/C++ low-level optimization, CUDA, or parallel compute systems. Benefits
This role offers competitve pay, hybrid work options, and comprehensive health benefits.
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