{"id":3448107,"url":"https://alion.io/job/ahead-senior-engineer-compute","title":"Senior Engineer, Compute","company":{"id":3614,"name":"AHEAD","domain":"ahead.com","url":"https://alion.io/company/ahead","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":38,"ghost_share":0,"stale_share":0.816,"repost_share":0,"time_to_fill_p50_days":74,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India","Bengaluru, India","Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":168},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Ansible","optional":false},{"name":"Bash","optional":false},{"name":"Configuration Management","optional":false},{"name":"CUDA","optional":false},{"name":"DHCP","optional":false},{"name":"DNS","optional":false},{"name":"HPC","optional":false},{"name":"Incident Management","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"SLURM","optional":false},{"name":"TCP/IP","optional":false},{"name":"Terraform","optional":false},{"name":"Unix","optional":false},{"name":"C","optional":true},{"name":"CI/CD","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GitOps","optional":true},{"name":"Helm","optional":true},{"name":"Immutable Infrastructure","optional":true},{"name":"InfiniBand","optional":true},{"name":"LLM","optional":true},{"name":"MPI","optional":true},{"name":"NCCL","optional":true},{"name":"OpenMP","optional":true},{"name":"PyTorch","optional":true},{"name":"RAG","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-27T10:29:29Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-11-03T05:00:09Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"The High-Performance Computing Compute Engineer is primarily responsible for the overall health and maintenance of the infrastructure that powers high-performance computing, artificial intelligence, machine learning, and large-scale data workloadsin our managed services customer environments. Our HPC Compute Engineers are a valued member of the Managed Services Infrastructure Practice responsible for Tier 3 incident management, service request management and change management infrastructure support for all Managed Services customers.\nThe successful candidate will own critical components of the compute environment-from Linux hosts, GPUs, HPC clusters, and physical hardware through schedulers, containers, monitoring, automation, and version maintenance. The engineer will work closely with our internal infrastructure teams, vendors, and customers to deliver reliable, secure, and efficient AI compute services.\nKey Responsibilities\nProvide enterprise-level operational support to Managed Services customers for incident, problem, and change management activities\n\nManage and maintain large-scale HPC and GPU compute clusters for AI training, inference, simulation, and data-intensive workloads\n\nDeploy and administer Linux-based compute environments, including operating systems, drivers, firmware, CUDA or equivalent accelerator software, libraries, and runtime dependencies\n\nOperate workload scheduling and orchestration platforms such as Slurm, Kubernetes, Ray, or comparable technologies\n\nBenchmark and tune end-to-end workload performance\n\nTroubleshoot complex failures from bare metal and firmware through the operating system, drivers, middleware, distributed frameworks, and user applications in step with other teams\n\nBuild automation for provisioning, configuration management, patching, validation, monitoring, and lifecycle management using Python, Bash, Ansible, Terraform, or similar tools\n\nDevelop operational dashboards, alerts, health checks, and capacity-planning processes that improve availability and resource utilization\n\nEstablish repeatable processes for incident response, root-cause analysis, change management, and operational readiness\n\nCreate technical documentation, runbooks, architecture diagrams, and user guidance for HPC and AI platforms\n\nParticipate in on-call rotation\n\nCompletion of training and certification as assigned to further skills and knowledge\n\nRequired Qualifications\nBachelor’s degree in computer science, computer engineering, electrical engineering, or a related technical field; equivalent practical experience may be considered\n\nFour or more years of experience deploying, operating, or engineering HPC, GPU, distributed systems, or cloud infrastructure\n\nStrong Linux or UNIX systems administration experience in production environments\n\nHands-on experience with GPU computing, accelerator infrastructure, or large-scale AI workloads\n\nExperience with at least one workload manager or orchestrator, such as Slurm, Kubernetes, Run:ai, or PBS\n\nStrong scripting and automation skills in Python, Bash, or a comparable language\n\nWorking knowledge of networking fundamentals, including TCP/IP, DNS, DHCP, routing, firewalls, Ethernet, and high-speed interconnects\n\nExperience diagnosing performance, reliability, and capacity issues across multiple infrastructure layers in a physical datacenter environment\n\nAbility to document systems clearly and communicate effectively with both technical and nontechnical stakeholders\n\nDemonstrated ability to work independently, prioritize operational risk, and collaborate in a fast-moving environment\n\nPreferred Qualifications\nExperience with NVIDIA GPU platforms, CUDA, NCCL, DGX, HGX, or comparable accelerator technologies\n\nExperience with InfiniBand, RoCE, RDMA, congestion control, or lossless network fabrics\n\nExperience with Slurm configuration, fair-share scheduling, QoS, accounting, job arrays, and high-throughput workload management\n\nExperience with Kubernetes, GPU operators, container runtimes, Helm, or cloud-native infrastructure\n\nExperience with parallel and distributed storage platforms such as Lustre, GPFS, BeeGFS, Ceph, ZFS, or XFS\n\nFamiliarity with PyTorch, TensorFlow, MPI, OpenMP, UCX, NCCL, Ray, or distributed model-training frameworks\n\nExperience with observability platforms, telemetry pipelines, and performance-profiling tools\n\nExperience with configuration management, infrastructure as code, CI/CD, GitOps, or immutable infrastructure practices\n\nExperience supporting LLM training, fine-tuning, retrieval-augmented generation, model serving, or inference 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