{"id":2590601,"url":"https://alion.io/job/univers-ai-engineer","title":"AI Engineer","company":{"id":175386,"name":"Univers","domain":"univers.com","url":"https://alion.io/company/univers-3","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Breezy","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-10-15T02:16:58Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-15T17:11:23Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"ABOUT UNIVERS\nUnivers combines global operating scale, deep industrial intelligence, and recognition from the institutions shaping energy, technology, and sustainability. The platforms built for the last decade were designed to monitor.\nUnivers was built to act - not just report\nUnivers operates at global scale:\n1,070 GW+ Energy assets under AI management\n450M+ Connected sensors and devices\n800+ Enterprise customers\nLeader Gartner Magic Quadrant Leader\nROLE OVERVIEW / THE OPPORTUNITY\nUnivers Global AI Lab\nAs an AI Engineer at the Global Impact AI Lab, you will design and develop scalable AI systems that bridge advanced AI research and real-world deployment across enterprise and industrial verticals. This role sits at the intersection of applied AI research, system architecture, and production integration.\nYou will work closely with Applied AI and AIoT Scientists to transition validated prototypes into robust, scalable systems, collaborating with product and engineering teams to ensure smooth integration into production environments.\nOperating in both 0→1 and scaling environments, you will build and refine model training and inference pipelines, develop agentic frameworks that coordinate reasoning and workflow execution, and architect AI systems that meet real-world requirements for latency, throughput, scalability, and runtime efficiency.\nYou will play a critical role in defining how AI systems are engineered so they can move from lab validation to production adoption, ensuring performance, reliability, and maintainability across cloud, hybrid, and edge deployments.\nThis role requires strong systems thinking, engineering discipline, and the ability to translate advanced AI capabilities into deployable, high-performance systems.\nRESPONSIBILITIES/WHAT YOU WILL DO\n\nPartner with Applied AI and AIoT Scientists to transition prototypes into scalable, production-aligned architectures\nDesign and implement end-to-end training, evaluation, and inference pipelines\nDevelop and refine agentic frameworks that orchestrate model reasoning, tool integration, and workflow execution\nOptimize foundation model inference performance using techniques such as KV caching, batching strategies, quantization, and optimized runtimes (e.g., vLLM or similar frameworks)\nDefine and architect systems that meet real-world constraints, including latency, throughput, scalability, and runtime efficiency\nEstablish best practices for AI infrastructure, including containerization (Docker), orchestration (Kubernetes), and model lifecycle management\nCollaborate with product and engineering teams to integrate validated AI capabilities into scalable platform components\nEngage with technical stakeholders to ensure system performance and deployment readiness\nREQUIREMENTS/WHAT WE ARE LOOKING FOR\nBachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, or a related field (or equivalent industry experience)\nStrong proficiency in Python and experience with PyTorch for model development and experimentation\nExperience building and optimizing training and inference pipelines for machine learning systems\nFamiliarity with MLOps and model lifecycle management practices\nExperience working with containerization and orchestration technologies such as Docker and Kubernetes\nExperience optimizing foundation model inference performance and understanding runtime efficiency trade-offs\nStrong understanding of system-level considerations, including latency, throughput, scalability, and reliability\nAbility to collaborate effectively across research, product, and engineering teams\nExcellent communication 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