{"id":68306,"url":"https://alion.io/job/tsc-ai-engineer","title":"AI Engineer","company":{"id":25925,"name":"TSC","domain":"tsc.ai","url":"https://alion.io/company/tsc","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"BambooHR","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ho Chi Minh City, Vietnam"],"countries":["VN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Confluence","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Jira","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-07-27T00:00:00Z","employer_posted_date":"2026-07-27","last_verified_at":"2026-10-28T02:45:55Z","board_verified":true,"closed_at":null,"days_open":93,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":93},"description":"Vietnam | Hybrid, Ho Chi Minh City | Reports to: Head of AI\nRole Overview\nTSC builds AI-native stakeholder and issue intelligence products for enterprise teams operating across public affairs, regulatory, reputational and external-risk environments. We are hiring an AI Engineer to build and operate the AI systems behind Genie, turning fragmented, fast-changing data into reliable and actionable intelligence.\nThis is a product-focused engineering role. You will move from rapid experimentation to production delivery, working across LLM applications, retrieval, machine learning, data pipelines, APIs, evaluation and observability. You will work closely with the Head of AI, Product, Data Engineering and Platform Engineering across Vietnam and Singapore.\nSuccess is not measured by the novelty of a model or the sophistication of a demonstration. It is measured by whether the system produces accurate, traceable and useful outputs for customers, operates safely, and performs at sustainable cost and latency.\nThe Mandate\nBuild production AI capabilities that convert noisy public, proprietary and customer data into dependable stakeholder and issue intelligence.\nWhat You Will Own\n1. Production AI Systems\nDesign, build, test and operate AI services and workflows from proof of concept through production deployment.\nDevelop capabilities across classification, extraction, summarisation, entity resolution, stakeholder mapping, retrieval, risk detection and insight generation.\nSelect the appropriate combination of deterministic software, machine learning, retrieval, LLMs, agents and human review for each use case.\nBuild clear APIs and integrations that connect AI capabilities to Genie and related internal systems.\nOwn the quality, reliability and maintainability of the components you ship, including production support and continuous improvement.\n2. Data, Retrieval and Model Quality\nBuild and improve pipelines for ingestion, cleaning, enrichment, labelling, chunking, indexing and retrieval.\nImprove retrieval relevance, grounding, entity resolution and freshness across fragmented real-world datasets.\nFine-tune or adapt specialised and open-source models where this provides a clear quality, privacy, latency or cost advantage.\nCreate reusable datasets, prompts, configurations and evaluation artefacts rather than relying on one-off experiments.\nPartner with Data Engineers and domain experts to turn feedback and edge cases into systematic quality improvements.\n3. Evaluation, Reliability and Safety\nDefine and implement automated and human evaluation for AI features, including golden datasets, regression tests and task-specific quality metrics.\nInstrument systems with logs, traces, latency and token monitoring, quality signals and actionable failure diagnostics.\nDesign appropriate fallbacks, retry behaviour, human-review thresholds, rollback paths and incident escalation.\nProtect sensitive and customer-specific data through secure handling, access controls, workspace boundaries and careful use of external model providers.\nIdentify and mitigate risks including prompt injection, data leakage, stale context, unsupported claims, excessive autonomy and unsafe tool permissions.\n4. Product Delivery and Technical Judgement\nTranslate ambiguous customer and business workflows into clear technical requirements, experiments and acceptance criteria.\nAsk who will use an output, what decision it supports, what failure would cost, and how its value can be measured.\nWork with Product to sequence experiments and releases based on customer value, quality, feasibility and operational risk.\nCommunicate technical trade-offs, limitations and evidence clearly to both technical and non-technical colleagues.\nContribute to architecture and roadmap decisions through working software, evaluation evidence and documented recommendations.\n5. Agentic and AI-Assisted Engineering\nBuild bounded agentic workflows with explicit tool permissions, validation steps, stopping conditions, budgets and human approval points.\nUse coding assistants and engineering agents to improve delivery speed while remaining accountable for design, security, testing and code quality.\nPrefer deterministic orchestration when it is safer, cheaper or easier to test than an autonomous agent.\nEvaluate emerging tools through controlled experiments and adopt them only when they improve measurable product or engineering outcomes.\n6. Global Engineering Collaboration\nWork as a core member of TSC's global engineering team, with regular collaboration across Vietnam and Singapore.\nMaintain clear technical documentation, implementation notes and decision records in shared systems such as Jira and Confluence.\nParticipate actively in code reviews, design reviews, sprint planning, incident learning and architectural discussions.\nUse overlapping working hours for high-value collaboration while preserving focused execution through strong asynchronous communication.\nWhat Success Looks Like\nWithin the first 6 months, you will have:\nShipped and operated material AI capabilities used in live customer or internal workflows.\nEstablished evaluation and observability for the features you own, with clear quality baselines and regression controls.\nImproved at least one critical dimension of a production workflow, such as accuracy, retrieval quality, latency, cost, traceability or human-review effort.\nStrengthened reusable data, retrieval and model components rather than accumulating isolated prototypes.\nDemonstrated reliable ownership across design, implementation, documentation, deployment and production support.\nBuilt trusted working relationships with Product, and Engineering across Vietnam and Singapore.\nRequirements\nMust-Have\nTypically 4+ years of experience in software, data, machine learning or AI engineering, with evidence of shipping and operating production systems. Stronger direct experience may substitute for a specific tenure threshold.\nProfessional proficiency in Python and practical experience building tested, maintainable services or data workflows. Strong SQL capability is expected.\nHands-on experience with LLM applications, retrieval-augmented generation, embeddings, structured extraction, orchestration and evaluation.\nExperience with data pipelines, APIs, version control, automated testing, CI/CD and cloud environments such as GCP or AWS.\nWorking knowledge of production observability, including logging, tracing, latency, failures, model usage and cost monitoring.\nSound judgement around sensitive data, model limitations, prompt injection, tool permissions, human review and rollback.\nProfessional English communication skills, particularly for technical documentation, code review and cross-border architectural discussion.\nA bachelor's degree in computer science, engineering or a related field, or equivalent practical experience.\nStrong Signals\nYou have built AI products that use classification, named-entity recognition, entity resolution, summarisation, extraction or recommendation over messy real-world data.\nYou have created evaluation datasets or automated quality checks for LLM, RAG or machine-learning systems.\nYou have used orchestration frameworks such as LangGraph or LangChain, while remaining able to design systems independently of a specific framework.\nYou have deployed or fine-tuned open-source models and understand the trade-offs against managed model APIs.\nYou have improved production model quality, latency or cost through routing, caching, batching, context management or model selection.\nYou have worked in a multi-tenant enterprise environment or with confidential customer data.\nYou can show production work, open-source contributions, technical writing or well-explained experiments that demonstrate how you solve engineering problems.\nThis Role Is Not\nA research-only role focused primarily on novel models, publications or benchmark performance.\nA prompt-engineering role that stops at demonstrations and does not own code, data, evaluation or production reliability.\nA framework-chasing role where new tools are adopted without evidence of customer or engineering value.\nA narrow modelling role isolated from APIs, data pipelines, deployment and the wider product lifecycle.\nWhy Join TSC\nBuild AI systems for real enterprise decisions, not speculative demonstrations.\nWork directly with the Head of AI, Product and senior engineering leaders across Vietnam and Singapore.\nHelp shape TSC's AI engineering practices and the continued development of its Vietnam engineering capability.\nWork in a hybrid model that combines office collaboration in Ho Chi Minh City with focused remote execution.\nReceive a competitive local package, equity options and a 13th-month bonus.\nDevelop across applied AI, data engineering, product judgement and enterprise-grade delivery.\nThe Core Question\nCan you turn an ambiguous intelligence problem and messy data into a production system that users can trust, engineers can operate, and the business can 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