Senior AI Engineer

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NetCraftsmen, now BlueAlly

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51 - 200 Mitarbeiter

NetCraftsmen wurde im März 2022 von BlueAlly übernommen. Bei BlueAlly ist es unsere Mission, Technologie für jedes Unternehmen zugänglicher, sicherer und wirkungsvoller zu machen. In einem digitalen Zeitalter, in dem fortschrittliche Technologie oft mit zusätzlicher Komplexität einhergeht, kann die Einführung von Next-Gen-IT eine anspruchsvolle Aufgabe sein. BlueAlly bietet ein umfassendes Spektrum an IT-Management- und Beratungs-/Dienstleistungen, die es Organisationen ermöglichen, von den Vorteilen fortschrittlicher Technologie zu profitieren. Von Cloud über Cybersicherheit, Infrastruktur bis hin zu Anwendungsmodernisierung – wir florieren dank modernster Technologien und Dienstleistungen. Erhöhen Sie den Einfluss von Technologie in Ihrem Unternehmen mit erstklassiger Expertise, die bahnbrechende Erkenntnisse liefert. Wandeln Sie komplexe Entscheidungen in klare Chancen um mit einem vertrauenswürdigen Technologie-Guide, der sicherstellt, dass der nächste digitale Fortschritt Ihr entscheidender Vorteil sein wird. Tauschen Sie IT-Komplexität gegen Fähigkeit ein mit Lösungen, die Möglichkeiten erweitern, und schreiten Sie sicher voran, im Wissen, dass Sie BlueAlly als Ihren Verbündeten bei Ihrem nächsten Schritt haben. BlueAlly. Erobern Sie die Komplexität.

Beschreibung

• Design, build, and operate enterprise AI systems across our client portfolio. • Work end-to-end across the AI stack — from inference engines and platform infrastructure up through application-level engineering. • Lead end-to-end design, build, and operation of AI systems on AI Factory platforms across multiple client engagements. • Engineer and tune LLM inference serving stacks — primary depth in vLLM with breadth across the inference ecosystem — for client latency, throughput, and cost targets. • Tune inference performance through KV cache management, paged attention, batching strategies, and Dynamo-based disaggregated serving. • Architect and operate MLOps pipelines covering model lifecycle, registries, deployment, rollback, and observability. • Design and engineer RAG applications on top of vector databases. • Build and tune prompt-engineering patterns at production scale. • Engineer high-performance storage and networking for AI workloads. • Operate Kubernetes clusters underpinning AI workloads. • Build and maintain container images, registries, and CI/CD pipelines for AI/ML services. • Implement monitoring, alerting, logging, and capacity planning across the AI stack. • Harden environments to meet client security and compliance requirements. • Lead troubleshooting across various environments and technologies. • Engage directly with client stakeholders — technical and executive — to communicate status, root cause, options, and recommendations. • Mentor and code-review work from less senior engineers; raise the technical bar of every engagement you join. • Author runbooks, reference architectures, and knowledge base content; lead client knowledge transfer and enablement sessions. • Participate in on-call rotation and incident response for production AI workloads. • Contribute reusable patterns, tooling, and reference designs back to the practice.

🎯 Anforderungen

• 7+ years of software, data, or infrastructure engineering, with 3+ years specifically working with modern AI / LLM systems. • Production-quality Python at engineering level — testing, code review, version control fluency, and shipping code that other engineers depend on. • Deep production Linux experience, including system internals, performance tuning, and troubleshooting. • Deep proficiency with Docker — image build, registry management, runtime tuning, and container security. • Strong server-platform skills including CPU/GPU topologies, PCIe, BMC management, BIOS/firmware lifecycle, and physical-to-logical troubleshooting. • Hands-on experience deploying and operating one or more of HPE PCAI, Dell AI Factory, or Nutanix Enterprise AI. • Production experience deploying, tuning, and operating vLLM. • Working knowledge of multiple inference and model-serving frameworks beyond vLLM, with the ability to choose and tune the right tool for each workload. • Hands-on experience with high-throughput, low-latency storage and network fabrics for AI workloads — including RDMA-class interconnects, parallel/object storage tiers, KV cache management, and Dynamo-style disaggregated serving. • Practical experience operating MLOps tooling and patterns — model registries, deployment pipelines, GitOps, lineage, and rollback. • Hands-on experience deploying, tuning, and integrating vector databases and RAG pipelines, including the application-level engineering that sits on top of them. • Production experience designing system prompts, structured output, function calling, and tool-using LLM patterns. • Demonstrated experience designing LLM evaluation harnesses — golden sets, regression suites, and quality/cost metrics. • Demonstrated ability to engage directly with client stakeholders — running working sessions, presenting recommendations, and translating technical detail for non-technical audiences. • Strong written and verbal communication — clear reference architectures, runbooks, and incident reports. • Track record of mentoring more junior engineers and raising team technical quality through code review and pairing. • TCP/IP, DNS, load balancing, VLANs, and firewall administration. • Comfort working across multiple concurrent client environments and managing competing priorities under SLA.