{"id":101372,"url":"https://alion.io/job/jio-star-senior-software-development-engineer-devops-video-insights","title":"Senior Software Development Engineer (DevOps) - Video Insights","company":{"id":8901,"name":"JioStar","domain":"jiostar.com","url":"https://alion.io/company/jio-star","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":77,"open_postings":45,"ghost_share":0,"stale_share":0.911,"repost_share":0.022,"time_to_fill_p50_days":43,"computed_at":"2026-10-17T05:46:00Z"}},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":43},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Go","optional":false},{"name":"Helm","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"MLFlow","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Terraform","optional":false},{"name":"Edge AI","optional":true}],"status":"live","first_seen_at":"2026-06-01T00:00:00Z","employer_posted_date":"2026-06-01","last_verified_at":"2026-10-17T20:46:53Z","board_verified":true,"closed_at":null,"days_open":138,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":138},"description":"Job Summary:\nYou are a systems-oriented engineer passionate about building reliable, scalable infrastructure for AI and ML workloads. You have deep expertise in cloud platforms, containerization, and distributed systems. You thrive on solving complex operational challenges and are motivated by enabling engineering teams to ship reliably and efficiently. You constantly seek to improve systems for reliability, observability, and developer experience.\nThe pace of our growth is incredible - if you want to build world-class infrastructure for cutting-edge video AI systems at scale, join us!\nAbout the team:\nJoin our Video CoE as a Senior DevOps Engineer and build the infrastructure that powers next-generation video AI systems. You'll architect and maintain systems that enable ML engineers to rapidly iterate and deploy state-of-the-art video understanding models at massive scale. This role combines deep systems expertise with exposure to cutting-edge AI technology, offering the opportunity to shape how modern video AI infrastructure is built and deployed.\nKey Responsibilities:\nDesign and implement robust, scalable cloud infrastructure for deploying video AI models and services\nBuild and maintain Kubernetes clusters and containerized deployment pipelines optimized for GPU workloads\nEstablish monitoring, logging, and observability systems for AI/ML production services\nDevelop infrastructure-as-code practices and automation for rapid, reliable deployment\nOptimize cloud costs while maintaining performance and reliability standards\nWork with ML engineers to develop efficient MLOps pipelines for model training, evaluation, and serving\nEstablish and maintain security, compliance, and data governance practices for AI systems\nMentor engineering teams on infrastructure best practices and cloud architecture patterns\nDrive incident response, postmortems, and continuous improvement of system reliability\nSkills and attributes for success:\n4+ years of infrastructure, DevOps, or cloud platform engineering experience\nDeep hands-on expertise with Kubernetes and container orchestration (Docker, Kubernetes)\nExpert proficiency in at least one major cloud platform (AWS, GCP, or Azure)\nStrong programming background in Python, Go, or similar languages\nDeep understanding of distributed systems, networking, and storage concepts\nExperience with GPU infrastructure, CUDA, or other accelerated computing environments\nHands-on experience with ML infrastructure tools and frameworks (Kubeflow, MLflow, Ray, or similar)\nStrong understanding of infrastructure-as-code tools (Terraform, CloudFormation, Helm)\nExperience with CI/CD pipelines and automation frameworks\nPassion for monitoring, logging, and observability - 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