Role Description The Root Cause Engineer (RCA), Mid performs structured root cause analysis for recurring, chronic, or high-impact incidents to identify underlying technical, process, or architectural issues affecting mission-critical federal IT services. This role collects and correlates evidence across logs, traces, metrics, configuration data, and incident records to distinguish underlying causes from symptoms and reconstruct incident timelines. The engineer collaborates with Incident Response, Problem Management, SRE, and engineering teams to document RCA outcomes and define actionable corrective and preventive measures that improve service reliability and reduce recurrence. - Apply common RCA methodologies (such as 5 Whys, fishbone diagrams, fault tree analysis, and component failure impact analysis) and select appropriate techniques based on incident complexity and impact. - Gather and analyze monitoring data, logs, traces, configuration records, service topology maps, and incident timelines, to distinguish contributing factors from true root causes. - Facilitate cross-functional RCA sessions with operations, engineering, cybersecurity, and business teams to drive focused discussion, managing differing viewpoints, and converging on agreed causes and remediation actions. - Translate RCA findings into corrective and preventive actions aligned with Problem Management workflows. - Define and track RCA metrics such as recurrence rates, RCA cycle time, and others using data driven insights to improve analysis quality, timeliness, and effectiveness. - Support integration of RCA activities into ITIL-aligned Problem Management and continual service improvement practices. - Produce high-quality RCA reports that are audience‑appropriate describing what happened, why it happened, and prevention steps. - Identify systemic reliability risks and patterns across incidents. Qualifications - Bachelor’s degree in IT, Computer Science, Business Administration, or related field, or equivalent relevant experience. - 4–7 years of experience in IT operations, incident/problem management, reliability engineering, or related roles with significant responsibility for conducting structured RCAs. - Strong understanding of ITIL principles, incident and problem management best practices, and proficiency with incident and problem management tools. - Demonstrated expertise in at least one structured RCA methodology and ability to coach teams in its use. - Strong analytical, problem‑solving, facilitation and communication skills with the ability to manage multiple concurrent investigations effectively. - Ability to work collaboratively with cross‑functional technical and business teams in a fast‑paced enterprise IT environment. - Active or obtainable SECRET clearance and U.S. citizenship, with less than 10% travel required. Preferred Qualifications - Hands-on RCA experience in complex enterprises or federal environments. - Formal training in RCA or structured problem-solving techniques. - Experience using observability platforms, log analytics, and monitoring tools to drive data‑driven incident reconstruction and analysis. - Familiarity with reliability engineering concepts (such as SLOs, error budgets, and resiliency patterns) and how they inform RCA priorities and recommendations. Compensation Ranges 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. EEO Requirements 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. Physical Requirements The physical requirements described in "Knowledge, Skills and Abilities" above are representative of those which must be met by an employee to successfully perform the primary functions of this job. Reasonable accommodations may be made to enable individuals with qualifying disabilities, who are otherwise qualified, to perform the primary functions. Disclaimer The preceding job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this job.

• Own all technical aspects of the customer lifecycle—from deployment through activation, making sure our customers are successful using Devin • Work closely with customers to understand their technical challenges and identify the best use cases for Devin to be a productive contributor • Build deep relationships with our customers’ engineering teams to ensure smooth product integration and adoption, activating top users and driving product utilization • Build! Always listen to customer feedback, have a bias towards implementing requested features yourself, and advocate for the customers’ needs with the rest of the team • Be the customer oracle! Exercise your product instincts to help set the roadmap

• Own the technical success of GSI partners across onboarding, enablement, and scaled delivery of Devin/Windsurf • Work directly with partner engineering and architecture teams to understand their delivery models, tooling, and customer use cases • Enable partners to build repeatable solutions, accelerators, and reference architectures using Devin/Windsurf • Lead technical trainings, workshops, and deep dives for partner teams (hands-on, not slideware) • Support partner-led customer pilots and early production deployments, stepping in where complexity is highest • Act as a technical escalation point for complex integrations, security reviews, CI/CD environments, and enterprise workflows • Build strong, trust-based relationships with partner technical leaders in GSIs (e.g., delivery heads, principal engineers, solution architects) • Capture field learnings and partner feedback to influence product roadmap, packaging, and partner readiness • Proactively identify gaps in partner capability and work with internal teams to close them

• The AI Engineer will play a key role in designing, developing, and deploying generative artificial intelligence solutions for real business applications. • The position focuses on building Retrieval-Augmented Generation (RAG) pipelines, integrating Large Language Models (LLMs) into production systems, and collaborating with cross-functional teams to deliver end-to-end AI products. • The professional will also contribute to responsible AI practices and to the continuous advancement of the organization's AI capabilities. • Design, build, and optimize RAG pipelines, including ingestion, chunking, embeddings generation, and data retrieval. • Integrate and orchestrate LLMs using frameworks such as LangChain and LlamaIndex. • Develop and manage vector database infrastructures, such as Pinecone, ChromaDB, FAISS, pgvector, or Weaviate. • Build evaluation frameworks to measure model accuracy, latency, groundedness, and cost. • Apply prompt engineering techniques to reduce hallucinations and improve response quality. • Develop agentic AI solutions with robust control mechanisms (guardrails), either from scratch or using existing frameworks. • Collaborate with Data Engineers and Full Stack Developers to deliver end-to-end AI applications. • Stay up to date with advances in generative AI, recommending new tools, techniques, and best practices. • Support responsible AI practices, including bias detection, safe handling of outputs, and considerations around PII (personally identifiable information).