System Engineer, Behavioral Analysis

Role Overview As a Senior Software Engineer, AI Ops at Scale AI, you will own the long-term technical health, performance, and stability of AI solutions deployed across our strategic public sector partners. While our Delivery Teams build and launch new use cases, you are the technical steward ensuring these deployments operate with Operational Excellence. You will bridge software engineering, MLOps, and client governance, managing tiered SLAs, tracking model drift, and executing maintenance protocols that protect both system integrity and operational margins. Key Responsibilities Handover Gate & Onboarding: Act as the technical gatekeeper during the formal transition from Delivery to Maintenance. Conduct deep-dive reviews to ensure baseline code, prompts, and architecture meet strict maintainability and documentation standards before sign-off. Tiered SLA & Incident Management: Own technical response and resolution targets across multi-tiered service models (from Business-Hours Essential to 24/7 Mission-Critical). Lead Incident Governance, Root Cause Analysis (RCA), and P1/P2 mitigations within strict active support windows. AI Lifecycle Governance: Monitor production model performance, latency, and data drift. Manage prompt configuration repositories to maintain behavioral consistency and perform regression testing when LLM providers update underlying endpoints. Request Classification & Technical Scope: Operationalize the boundary between Routine Maintenance (In-Scope) and System Evolution (Out-of-Scope). Assess incoming client requests and run comparative benchmarking on new AI models. Automation & Reliability Engineering: Eliminate operational toil by engineering self-healing data pipelines, automated RAG indexing syncs, and telemetry tooling. Influence upstream "Delivery" teams to adopt architectural patterns that simplify ongoing maintenance. Client Technical Interface: Serve as the senior technical point of contact for government and enter

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Roles and Responsibilities Verification of Analog and mixed signal circuits. Generation of a Full chip mixed signal simulation environment where analog, digital and FW co-exist. Schematic/behavioral model generation of certain blocks to make FC simulation feasible/efficient Device level electrical rule checks – SOA, snapback, sfb etc Static and dynamic simulations to identify the leakage paths SPF extraction and fanout, coupling checks Power cycling simulations – PL and Brown out events Chip level user mode simulations correlating analog subsystems, logic and FW algorithms Design data sheet review and generation of verification plan You will also work and support the efforts of groups such as Product Engineering, Test, Probe, Process Integration, Assembly and Marketing to proactively design products to optimize all manufacturing functions and assure the best cost, quality, root-cause analysis, reliability, time-to-market, and customer satisfaction. Requirements Understanding of MOS device physics mainly second order effects Basic knowledge of few analog circuits – CM, BGR, op-amp, regulators, ADC, DAC and charge pumps Familiarity with shell, Perl/python scripts Familiarity with virtuoso, hspice, xa and cosim Familiarity with HDLs – Verilog/SV and Verilog A/AMS Debug enthusiast with system level outlook Ability to grab complex workflows and constant drive to innovate

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Roles and Responsibilities · Plan and execute verification of analog and mixed-signal circuits at block, subsystem, and full-chip levels. · Develop and maintain full-chip mixed-signal simulation environments in which analog circuitry, digital logic, and firmware coexist. · Create schematic and behavioral models for selected blocks to make full-chip simulations feasible, efficient, and sufficiently accurate for the intended verification objective. · Develop verification plans from design data sheets, new feature requests, requirements, interface definitions, and identified risk areas. · Run static and dynamic simulations to identify leakage paths, power-sequencing issues, and unintended circuit interactions. · Perform device-level electrical rule checks, including SOA, snapback, SFB, and related reliability checks. · Perform SPF extraction analysis and fanout, connectivity, and coupling checks. · Execute power-cycling simulations, including power-loss and brownout events. · Run chip-level user-mode simulations that correlate analog subsystems, logic behavior, and firmware algorithms. · Debug complex AMS failures with a system-level outlook, identify root causes, and work with design, firmware, and verification partners to drive closure. · Develop scripts and reusable automation for regression setup, simulation execution, log processing, waveform analysis, result comparison,

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Roles and Responsibilities · Plan and execute verification of analog and mixed-signal circuits at block, subsystem, and full-chip levels. · Develop and maintain full-chip mixed-signal simulation environments in which analog circuitry, digital logic, and firmware coexist. · Create schematic and behavioral models for selected blocks to make full-chip simulations feasible, efficient, and sufficiently accurate for the intended verification objective. · Develop verification plans from design data sheets, new feature requests, requirements, interface definitions, and identified risk areas. · Run static and dynamic simulations to identify leakage paths, power-sequencing issues, and unintended circuit interactions. · Perform device-level electrical rule checks, including SOA, snapback, SFB, and related reliability checks. · Perform SPF extraction analysis and fanout, connectivity, and coupling checks. · Execute power-cycling simulations, including power-loss and brownout events. · Run chip-level user-mode simulations that correlate analog subsystems, logic behavior, and firmware algorithms. · Debug complex AMS failures with a system-level outlook, identify root causes, and work with design, firmware, and verification partners to drive closure. · Develop scripts and reusable automation for regression setup, simulation execution, log processing, waveform analysis, result comparison,

For more than 30 years, SonicWall has delivered a partner-first unified cybersecurity portfolio that helps SMBs, MSPs, and IT teams consolidate network security, endpoint security, security service edge (SSE), and threat response across hybrid environments. By bringing connected technology, real-time threat intelligence, automation, and expert services together, SonicWall helps organizations simplify security operations, achieve better security outcomes, and lower total cost of ownership. Backed by a global ecosystem of more than 17,000 partners, SonicWall delivers enterprise-grade protection built for SMB and mid-market realities. For more information, visit www.sonicwall.com or follow us on X, LinkedIn , Facebook , and Instagram . We are seeking an experienced Principal Software Engineer to lead the architecture, development, and evolution of a large-scale malware analysis and cybersecurity platform. This role will own the end-to-end technical architecture across Python-based analysis pipelines, message-driven job orchestration systems, Windows VM sandbox environments, and web-based interfaces. The ideal candidate brings deep expertise in software engineering, malware analysis, and distributed systems, with a proven ability to take ownership of complex legacy platforms and drive modernization initiatives. You will lead the design and implementation of advanced detection capabilities, including behavioral analysis, malware sandboxing, machine learning-based classification, and support for new file types, while ensuring platform scalability, reliability, and performance. This position requires strong hands-on development skills in Python with Linux-based production environments, messaging architectures such as RabbitMQ, virtualization technologies, and cloud-native deployment practices. As the technical leader for the platform, you will establish engineering standards, guide architectural decisions, reduce technical debt, mentor senior engi

Join Adobe Security Engineering and help build the ML and generative AI capabilities that protect Adobe, our products, and our customers. Our team sits where cybersecurity, large-scale data, and AI meet, building models for anomaly detection, threat detection, investigation, and security analytics across some of Adobe's largest datasets. As a Staff Machine Learning Engineer, you'll design, build, and scale production ML systems spanning deep learning, behavioral modeling, embeddings, and agentic AI. You'll write code, train models, run experiments, and help shape the architecture the broader team builds on. You'll also partner closely with data, platform, and security engineers across Adobe. The Challenge Architect end-to-end ML systems for high-volume security data, turning experiments into reusable capabilities. Own the ML lifecycle, from feature engineering through training, deployment, monitoring, and retraining. Build LLM and agentic AI capabilities for security investigation, including retrieval-augmented generation. Design evaluation frameworks that measure model quality and help analysts trust and validate results. Work with data and platform engineers to scale pipelines and resolve system bottlenecks together. Mentor engineers and help guide technical direction through design reviews and hands-on collaboration. What you bring Experience running production ML systems from early experimentation through sustained operation at scale. A strong background training models with PyTorch and transformers, including behavioral modeling and anomaly detection. Comfort with distributed compute like Spark, cloud platforms like AWS, and MLOps tools like MLflow. Experience building with LLMs or generative AI, along with evaluating how they behave in production. Strong Python and SQL skills, and solid software engineering habit