Role Description The Storage Engineer, Junior supports the operation of enterprise storage systems that serve virtual machines, databases, and file services in a secure government environment. This role assists senior engineers with provisioning, monitoring, and troubleshooting SAN and NAS resources. The engineer performs routine health checks, capacity reviews, and documentation updates to keep storage inventories accurate and auditable and participates in incident response under guidance. - Perform standard storage tasks such as creating or expanding storage allocations, updating host mappings, and validating connectivity. - Monitor storage performance, utilization, and alerts using vendor or third-party tools and escalate issues as needed. - Maintain documentation of configurations, change records, and diagrams reflecting current storage environments. - Assist with backup, snapshot, and replication configurations in alignment with data protection policies. - Support routine health checks, capacity reviews, and basic troubleshooting activities. Qualifications - Bachelor’s degree in Information Technology, Computer Science, or a related field, or equivalent experience. - 0–3 years of experience in storage or systems administration roles. - Fundamental understanding of storage concepts (RAID, LUNs, volumes, shares) and storage technologies (SAN, NAS, cloud). - Basic familiarity with enterprise storage tools (for example, NetApp, EMC, AWS). - Good problem-solving, analytical, communication, and organizational skills. - Ability to obtain and maintain a Secret clearance; U.S. citizenship required. Requirements - Exposure to at least one enterprise storage platform and basic scripting for reporting or automation. - Familiarity with how storage changes affect virtual machine performance and availability. Benefits - Compensation ranges for ASM Research positions vary depending on multiple factors; including but not limited to, location, skill set, level of education, certifications, client requirements, contract-specific affordability, government clearance and investigation level, and years of experience. - The compensation displayed for this role is a general guideline based on these factors and is unique to each role. - Monetary compensation is one component of ASM's overall compensation and benefits package for employees. Company Description - It is the policy of ASM that an individual's race, color, religion, sex, disability, age, sexual orientation or national origin are not and will not be considered in any personnel or management decisions. - We affirm our commitment to these fundamental policies. - All recruiting, hiring, training, and promoting for all job classifications is done without regard to race, color, religion, sex, disability, or age. - All decisions on employment are made to abide by the principle of equal employment.

• Design and develop backend services using FastAPI / Flask. • Build and maintain internal and external APIs for both product features and AI-driven scenarios. • Work with PostgreSQL and PgBouncer: design schemas, optimize queries, and manage database load. • Utilize Redis for caching, queues, pub/sub, and other system-level tasks. • Develop asynchronous processes and background workers using Celery. • Containerize services with Docker and Docker Compose. • Manage orchestration with Docker Swarm and Kubernetes. • Deploy and maintain AI workers in GPU cloud environments, including RunPod and Vast.ai. • Set up CI/CD pipelines, monitoring, logging, and basic SRE practices. • Participate in designing scalable and fault-tolerant backend systems. • Make architectural decisions for high-load and AI-native components. • Contribute to the backend of the AI IDE itself, including integrations with AI models and RAG subsystems.

• Design and build MCP servers used by AI agents • Develop backend services using Python with asynchronous programming patterns • Implement REST APIs supporting agent orchestration workflows • Build event-driven integrations and webhook-based communication • Implement distributed state management using Redis • Troubleshoot and optimise reliability and performance of distributed AI systems

Role Description In this role, you will be at the forefront of the AI revolution, supporting a high-impact project focused on autonomous agents and intelligent backend architectures. You will join a collaborative, fast-paced team dedicated to building the next generation of AI-driven solutions. The project involves architecting sophisticated agentic workflows and leveraging advanced retrieval techniques to power business intelligence at scale. As a Backend Engineer (AI Agentic), you will be instrumental in transforming raw data and LLM capabilities into reliable, goal-oriented AI systems. Key Responsibilities - Agentic Development: Design and implement complex AI agents and multi-agent workflows using frameworks like LangChain or LangGraph. - Backend Architecture: Build and maintain scalable, high-performance backend services using Python to support AI-driven features. - RAG Implementation: Develop and optimize Retrieval-Augmented Generation (RAG) pipelines to provide agents with accurate, context-aware information. - Vector Database Management: Architect and manage vector storage solutions (Pinecone, Weaviate) to ensure efficient similarity search and data retrieval. - System Integration: Integrate Large Language Models (LLMs) with existing APIs and third-party services to enhance agent capabilities. - Agile Collaboration: Work closely with product owners, data scientists, and fellow engineers to translate ambitious AI requirements into production-ready code. Qualifications - AI Frameworks: Deep expertise in LangChain, LangGraph, or Semantic Kernel (3+ years of experience). - Backend Mastery: Senior-level proficiency in Python (6+ years of experience) and its ecosystem for building robust APIs. - Vector Databases: Proven experience working with Pinecone, Weaviate, or similar vector search engines. - AI Specialization: Strong understanding of Retrieval-Augmented Generation (RAG) and agentic design patterns. - Soft Skills: A truly collaborative team player mindset with an appetite for solving ambiguous, cutting-edge problems. - Fluent English: Excellent verbal and written communication skills for daily technical collaboration and documentation. Nice-to-Have Skills - Cloud & Orchestration: Experience deploying AI workloads on Kubernetes or Docker. - Prompt Engineering: Knowledge of advanced prompting techniques and LLM fine-tuning. - Data Engineering: Familiarity with data processing libraries like Pandas or Spark. - Monitoring: Experience with tools for monitoring LLM performance and cost (e.g., LangSmith). Time Zone & Collaboration This role is 100% remote from LATAM and requires consistent overlap with teams in Central Time (CT). Flexibility is expected to accommodate key ceremonies, stand-ups, and collaborative sessions within standard Austin business hours. Language All interviews, documentation, and daily communication are in English.