Über diese Senior Software Developer III Stelle bei Caseware
Contact
Maira Russo - Senior Talent Acquisition Partner
What You’ll Be Doing:
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You build it, you run it. End-to-end accountability for the AI platform in production: architecture, delivery, operations, cost, and quality, with on-call for what you ship. No hand-off to a separate ops or QA function.
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Design, build, and operate agentic AI systems: LLM services, retrieval pipelines, multi-agent orchestration, agent execution runtimes, and human-in-the-loop capabilities.
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Build and operate memory infrastructure for agent systems (storage, retrieval, promotion/demotion mechanics). Applied Science owns promotion criteria and scientific soundness.
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Own LLMOps: the harnesses, pipelines, and infrastructure that keep non-deterministic systems reliable and correct in production, including dynamic model selection by task, cost, and latency.
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Build automated eval infrastructure that continuously compares offline and online metrics and alerts on drift or regression. Applied Science owns the methodology and acceptance thresholds it checks against.
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Evaluate, adopt, and integrate third-party LLMOps and eval platforms where they accelerate delivery over building in-house.
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Build the technical controls, telemetry, and guardrails that enforce compliance frameworks (e.g., ISO 42001, AIUC-1) in the architecture itself.
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Build and maintain the agent definition management APIs and the tool registry definitions reference, keeping definitions stable as tools and models version independently.
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Lead the engineering build of proof-of-concepts. Applied Science defines the architecture bet and validation criteria being tested.
Core Responsibilities
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Create architecture, prototypes, and design proposals that deliver near-term business value while aligning with the future-state product and platform vision.
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Influence technical direction through architecture reviews, RFCs, design discussions, and hands-on collaboration.
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Partner with senior developers, architects, and tech leads to define and execute the product/platform architecture roadmap.
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Balance delivery speed, quality, maintainability, and long-term platform health when making technical decisions.
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Break down large initiatives into parallelizable chunks of work that deliver incremental business value.
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Champion proposals through all phases of the SDLC, from design through production implementation.
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Contribute to improving the core product build, CI/CD pipelines, and overall SDLC.
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Troubleshoot and eliminate root causes of persistent production issues; drive reliability, observability, monitoring, and incident response.
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Keep technical documentation current and create new artifacts as needed.
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Facilitate design discussions within and across teams.
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Represent the team in technical discussions with Tech Leads, Operations, Product/UX, Security, Domain SMEs, and external stakeholders.
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Act as a trusted technical leader, helping teams succeed through collaboration, influence, and shared ownership.
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Mentor other developers through design reviews, joint agentic-AI sessions, and feedback on skills, prompts, and other agent inputs; contribute to raising overall engineering maturity.
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Share knowledge of new technologies and industry best practices.
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Evaluate emerging technologies, frameworks, and cloud capabilities that could benefit the platform.
AI-Augmented Development Expectations:
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Use AI-assisted development tools — inline completion, conversational assistants, and agentic workflows — as a standard part of your daily workflow.
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Critically evaluate and take ownership of all AI-generated output before it ships — verify correctness, security, and alignment with architecture and team standards.
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Identify opportunities for agentic automation within your team, and lead the rollout of new AI-assisted workflows and tooling.
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Mentor other developers on effective, responsible use of AI-assisted development tools.
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Help establish team-level practices and guardrails for using AI tools safely and effectively.
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Build and refine feedback loops that improve the team's collective use of AI tooling over time.
What You Will Bring:
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2+ years building and operating production AI systems, with a working grasp of the latency, cost, accuracy, and reliability trade-offs involved.
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Experience with agent frameworks, agent memory systems, or orchestration of tool-using AI systems.
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Production AWS experience, including Infrastructure as Code (CDK, CloudFormation, or Terraform).
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Degree in Computer Science, Software Engineering, or equivalent practical experience; 8+ years of professional software development experience with demonstrated impact beyond a single team.
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Proven experience designing and operating distributed, cloud-native, SaaS/multi-tenant platforms at scale, ideally on AWS.
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Strong software fundamentals: OOP, design patterns, SOLID principles, and a track record of advocating for code quality.
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Experience with agentic-AI software development practices.
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Experience designing and consuming well-designed HTTP APIs (REST, GraphQL, or similar).
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Experience mentoring developers and influencing architectural decisions across teams.
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Strong written and verbal communication skills; comfortable operating in fast-moving environments with ambiguity and evolving requirements.
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Strong initiative to improve processes, tools, methodologies, and product quality.
Nice to Have
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Experience implementing AI guardrails, governance controls, and safety mechanisms, and translating compliance frameworks (e.g., ISO 42001, AIUC-1, NIST AI RMF) into technical controls.
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Experience evaluating and integrating third-party ML/LLMOps platforms, with sound buy vs. build judgment.
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Experience with retrieval systems (RAG), embedding pipelines, or hybrid search (vector + keyword).
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LLMOps experience: evaluation harnesses, automated offline/online metric comparison, drift and regression alerting.
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Experience operating systems in regulated or compliance-heavy domains.
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Familiarity with accounting, auditing, or financial workflows.
Tech Stack
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Reliability & Observability: New Relic, CloudWatch, Prometheus, OpenTelemetry or equivalent
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Cloud & Infrastructure: AWS, EKS, Lambda, S3, DynamoDB, IAM, VPC
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Containers & Orchestration: Docker, Kubernetes
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Automation & Scripting: Python, Bash, TypeScript or equivalent
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Incident & Operations: runbooks, alerting workflows, incident management tools, post-incident review practices
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Tooling: GitHub, GitHub Actions, Jira, Confluence, Microsoft Teams, Slack
