{"id":1281420,"url":"https://alion.io/job/onebyzero-applied-ai-engineer","title":"Applied AI Engineer","company":{"id":3036710,"name":"ONEBYZERO","domain":"onebyzero.ai","url":"https://alion.io/company/onebyzero","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","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":{"min":9000,"max":18000,"currency":"SGD","period":"month","gross":true,"usd_annual":169128},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Active Directory","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"FAISS","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LoRA","optional":false},{"name":"Milvus","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenSearch","optional":false},{"name":"Pinecone","optional":false},{"name":"Pre-training","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"SFT","optional":false},{"name":"Weaviate","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-09-07T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-09-07T00:00:00Z","board_verified":false,"closed_at":null,"days_open":20,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":20},"description":"About the Role\n\nWe are seeking a Deep Learning Architect with 4+ years of experience to help design and build production-grade GenAI systems. In this role, you will contribute architecture coverage across the team—reviewing system designs, identifying gaps, and guiding technical decisions at the solution level. You will work on end-to-end LLM system design, Retrieval-Augmented Generation (RAG) pipelines, and multi-agent architectures, with a strong focus on production readiness. Strong coding depth is non-negotiable.\n\nResponsibilities\n\nDesign and contribute to end-to-end LLM system architecture for real-world enterprise use cases (from requirements to production).\nPre-train, fine-tune LLMs and domain-specific models using techniques such as CPT, SFT, LoRA, and QLoRA for client-specific use cases.\nDesign and run model evaluation pipelines to benchmark performance, accuracy, and cost across different fine-tuning approaches.\nOptimise models for latency, throughput, token efficiency, and inference cost in production environments.\nWork alongside agent orchestration and architecture teams to integrate fine-tuned models into multi-agent pipelines.\nImplement prompt versioning, rollback strategies, and model monitoring to ensure reliability post-deployment.\nTranslate business requirements from client engagements into model adaptation strategies with clear success criteria.\nContribute to internal knowledge sharing on fine-tuning best practices, tooling, and emerging techniques.\nDefine enterprise integration patterns for GenAI systems (identity/access controls, auditability, data boundaries, governance, and compliance alignment).\nImprove production reliability: latency/throughput optimization, token efficiency, cost control, and robust failure handling.\nCollaborate with cross-functional stakeholders (engineering, data, product, client teams) to deliver high-impact solutions on tight timelines.\nContribute hands-on code, perform code reviews, and raise the engineering bar through strong software fundamentals.\n\nQualifications\n\n3–6 years of experience in ML engineering, LLMs, or model development roles.\nHands-on experience with Continual Pre-training (CPT), Supervised Fine-tuning (SFT), LoRA, or QLoRA on LLMs.\nStrong Python programming skills—ability to write clean, testable, production-ready code.\nExperience running model evaluation and benchmarking pipelines in a structured way.\nSolid understanding of transformer architectures and how fine-tuning affects model behaviour.\nExperience deploying fine-tuned and pre-trained models to cloud environments with attention to cost and latency.\nStrong problem-solving skills with the ability to work independently on client-facing projects.\nSolid software engineering fundamentals: APIs, data structures, testing, debugging, and performance optimization.\nAbility to review designs, communicate trade-offs clearly, and collaborate effectively in a fast-paced environment.\n\nRequired Skills\n\nExperience with AWS-native GenAI building blocks (e.g., Bedrock, OpenSearch, Lambda, ECS/EKS) and secure enterprise deployments.\nExperience with vector databases/search engines (OpenSearch, Pinecone, Weaviate, Milvus, FAISS) and retrieval optimization.\nExperience with containerization and orchestration (Docker, Kubernetes).\nExperience building evaluation/observability pipelines for LLM systems and implementing safety/guardrail patterns.\nConsulting or client-facing delivery experience.\nSkills\nAi, Retrieval-Augmented Generation (RAG), Cloudwatch, Amazon Bedrock, access permissions, Active Directory Federation Services, Communicating With Agents, Pipelines, AWS, special skill, Content Delivery Network Operations, Exchange Connectivity, Access Control Management, Security Processes, LLMs, CI/CD, Content Ingest Operations, AWS Lambda, Application Deployment, Skills Framework Adoption","description_format":"text","description_chars":3851,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Information Technology","AI Agents","Systems Integrators"],"lifecycle":[{"event":"open","at":"2026-09-26T02:00:33Z"}],"liveness":{"score":30,"band":"fade","label":"Fading","p_open":0.85,"p_active":0.641,"p_room":0.55,"age_days":19,"expected_fill_days":17,"reasons":["seen:19","win:tail"],"computed_at":"2026-09-26T05:45:00Z"},"pay":{"stated_usd_annual":169128,"is_top_pay":false},"html_url":"https://alion.io/job/onebyzero-applied-ai-engineer","json_url":"https://alion.io/job/onebyzero-applied-ai-engineer.json","meta":{"generated_at":"2026-09-27T01:15:10Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1109,"day_limit":5000,"remaining_today":3891,"minute_limit":60,"resets_at":"2026-09-28T00:00:00Z"}}}