{"id":11173,"url":"https://alion.io/job/ahead-ai-platform-engineer","title":"AI Platform Engineer","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":74,"open_postings":53,"ghost_share":0,"stale_share":0.849,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":48000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":8},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Ansible","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Configuration Management","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"GCP","optional":false},{"name":"HPC","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NVIDIA NeMo","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorRT","optional":false},{"name":"Terraform","optional":false},{"name":"Triton","optional":false}],"status":"live","first_seen_at":"2026-01-30T07:57:51Z","employer_posted_date":"2026-01-30","last_verified_at":"2026-10-17T06:28:19Z","board_verified":true,"closed_at":null,"days_open":259,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":259},"description":"We are seeking an experienced AI Platform Engineer to design, deploy, and optimize AI/ML infrastructure, AI workflows, and automated pipelines. This role focuses on building scalable environments for training and deploying machine learning models, leveraging modern orchestration, automation, and GPU acceleration technologies. You will collaborate with data scientists and platform engineers to drive efficient resource utilization and scalable operations across cloud and hybrid environments.\nKey Responsibilities\nKubernetes for AI/ML: Architect and manage Kubernetes clusters tailored to AI/ML workloads.\nGPU Orchestration: Implement Run:ai and operators for GPU resource orchestration and workload scheduling.\nAutomation & Pipelines: Develop and maintain Python-based automation scripts and ML pipelines; automate infrastructure provisioning with Terraform and configuration management with Ansible.\nNotebooks & Collaboration: Create and manage Jupyter Notebooks for experimentation and collaboration.\nNVIDIA Integration: Integrate and optimize NVIDIA Enterprise Suite components (CUDA, NeMo Framework, Triton, TensorRT, GPU drivers) for accelerated computing.\nMLOps Practices: Establish and maintain MLOps best practices for model lifecycle management, CI/CD, and monitoring (e.g., MLflow, Kubeflow).\nCollaboration: Work closely with data scientists and platform engineers to ensure efficient resource utilization and scalability across environments.\nRequired Skills & Experience\nStrong proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch).\nHands-on experience with Kubernetes and container orchestration.\nFamiliarity with Run:ai or similar GPU scheduling platforms.\nExpertise in Terraform and Ansible for infrastructure automation.\nExperience with Jupyter Notebooks for ML development.\nKnowledge of NVIDIA Enterprise Suite (CUDA, NeMo Framework, Triton, GPU drivers).\nSolid understanding of MLOps principles and tools (e.g., MLflow, Kubeflow).\nBackground in deploying and scaling AI workloads in cloud or hybrid environments.\nQualifications\n4+ years in platform architecture or solutions architecture, with 2+ years focused on AI/ML workloads.\nExperience with high-performance computing (HPC) environments.\nFamiliarity with distributed training and model optimization 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