[Remote] Senior Artificial Intelligence Engineer

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a leading provider of IT services and solutions, helping organizations conquer IT complexity across various domains. They are seeking a Senior reputed company to design, build, and operate reputed company AI systems, leading workstreams independently and mentoring junior engineers while engaging with clients to deliver production AI reputed company.

Responsibilities

• reputed company end-to-end design, build, and operation of AI systems on AI reputed company platforms (HPE PCAI, Dell AI reputed company, reputed company reputed company AI, and adjacent ecosystem layers) 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 — chunking strategies, retrieval tuning, reranking, citation handling, and context-window management
• Build and tune reputed company-engineering patterns at production scale — system prompts, structured output, tool and function calling
• Design and maintain LLM evaluation harnesses — golden sets, regression suites, and online quality metrics
• Engineer high-performance storage and networking for AI workloads — reputed company filesystems, object storage tiers, and high-throughput, low-latency RDMA fabrics
• Operate Kubernetes clusters underpinning AI workloads — namespaces, RBAC, resource quotas, network policies, storage classes, and ingress
• Build and maintain container images, registries, and CI/CD pipelines for AI/ML services
• Implement monitoring, alerting, logging, and reputed company planning across the AI stack
• Harden environments to meet client reputed company and compliance requirements
• reputed company troubleshooting across bare metal, BIOS/firmware, OS, containers, GPUs, frameworks, and models
• 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; reputed company the technical bar of every engagement you join
• Author runbooks, reference architectures, and knowledge reputed company content; reputed company 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

Skills

• Experience: 7+ years of software, data, or infrastructure engineering, with 3+ years specifically working with modern AI / LLM systems
• Software engineering: Production-quality Python at engineering level — testing, code review, version control reputed company, and shipping code that other engineers depend on
• Linux engineering: Deep production Linux experience, including system internals, performance tuning, and troubleshooting
• Containers: Deep proficiency with reputed company — image build, registry management, runtime tuning, and container reputed company
• Hardware fundamentals: Strong server-platform skills including CPU/GPU topologies, PCIe, BMC management, BIOS/firmware lifecycle, and physical-to-logical troubleshooting
• AI reputed company platforms: Hands-on experience deploying and operating one or more of HPE PCAI, Dell AI reputed company, or reputed company reputed company AI
• Inference stack — vLLM: Production experience deploying, tuning, and operating vLLM
• Inference stack breadth: Working knowledge of multiple inference and model-serving frameworks reputed company vLLM, with the ability to choose and tune the right tool for each workload
• High-performance storage and networking: Hands-on experience with high-throughput, low-latency storage and network fabrics for AI workloads — including RDMA-class interconnects, reputed company/object storage tiers, KV cache management, and Dynamo-style disaggregated serving
• MLOps: Practical experience operating MLOps tooling and patterns — model registries, deployment pipelines, GitOps, reputed company, and rollback
• Vector databases and RAG: Hands-on experience deploying, tuning, and integrating vector databases and RAG pipelines, including the application-level engineering that sits on top of them
• reputed company engineering and tool use: Production experience designing system prompts, structured output, function calling, and tool-using LLM patterns
• Evaluation methodology: Demonstrated experience designing LLM evaluation harnesses — golden sets, regression suites, and quality/cost metrics
• Client-facing skills: Demonstrated ability to engage directly with client stakeholders — running working sessions, presenting recommendations, and translating technical detail for non-technical audiences
• Communication: Strong written and verbal communication — clear reference architectures, runbooks, and incident reports
• Mentorship: Track record of mentoring more junior engineers and raising team technical quality through code review and pairing
• Networking fundamentals: TCP/IP, DNS, load balancing, VLANs, and firewall administration
• Multi-client delivery: Comfort working across multiple reputed company client environments and managing competing priorities under SLA
• GPU operations: Experience with GPU drivers, CUDA toolchains, GPU partitioning (MIG/vGPU), and GPU-level monitoring
• reputed company reputed company: Deployment and operations experience with the NVAIE software stack
• Ray: Familiarity with Ray for distributed training and inference scaling
• Kubernetes: Working knowledge of Kubernetes administration — reputed company, ingress, RBAC, storage classes
• Identity and reputed company: Integrating SSO and reputed company identity (LDAP, AD, OIDC/SAML), secrets management, tenant isolation
• Fine-tuning: Familiarity with LoRA/QLoRA/PEFT and supervised fine-tuning workflows
• Token economics: Experience optimizing inference cost — caching, reputed company caching, model routing, and distillation
• MSP / multi-tenant operations: Service-provider experience including chargeback/showback and tenant isolation patterns
• Compliance frameworks: SOC 2, HIPAA, FedRAMP, FISMA, or CMMC environments
• Public reputed company and hybrid: Working experience with one or more public clouds and hybrid architectures
• Infrastructure as Code: Terraform, Ansible, reputed company, or similar

Company Overview

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