{"id":1685149,"url":"https://alion.io/job/wing-bank-big-data-engineer","title":"Big Data Engineer","company":{"id":171507,"name":"Wing Bank","domain":"wingbank.com.kh","url":"https://alion.io/company/wingbank","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"Data Science","role_family":"Data Science","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":["Phnom Penh, Cambodia"],"countries":["KH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":32000,"max_usd":86000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":550},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Interpretability","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Post-training","optional":false},{"name":"Pre-training","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"RLHF","optional":false},{"name":"Sentiment Analysis","optional":false},{"name":"Tokenization","optional":false}],"status":"live","first_seen_at":"2026-10-02T10:20:01Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-04T02:49:17Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"JOB RESPONSIBILITIES\nConduct research and experimentation on state-of-the-art LLM architectures, data curation techniques, and training recipes. \nDevelop and implement novel approaches across the model lifecycle: pre-training, post-training (preference optimization, RLHF), prompt engineering, retrieval-augmented generation (RAG), agentic workflows, and memory engineering. \nApply cutting-edge NLP advancements to improve Khmer text understanding, including tokenization, word segmentation, and interpretability of model behavior on Khmer.\nIdentify, collect, and curate high-quality, diverse datasets in Khmer for LLM training, validation, and testing. \nBuild and maintain scalable data pipelines for large-scale collection, cleaning, deduplication, and filtering of Khmer text. \nGenerate and curate synthetic and augmented Khmer data to address gaps in low-resource domains. \nTrain and fine-tune Large Language Models using extensive Khmer datasets. \nOptimize LLM performance for tasks such as text generation, summarization, translation, question answering, and sentiment analysis in Khmer. \nCollaborate closely with MLOps Engineers to integrate trained LLMs. \nWork on containerization (Docker) and orchestration (Kubernetes) of LLM services. \nBuild and maintain data visualization and analytics pipelines to monitor model performance, training metrics, and data quality using enterprise big data tools. \nDesign and implement rigorous evaluation methodologies and metrics tailored for Khmer LLMs. \nConduct comprehensive testing and analysis to identify model biases, ethical concerns, and areas for improvement. \nJOB REQUIREMENTS\nBachelor's or Master's degree in Computer Science, Artificial Intelligence, Computational Linguistics, or a related field. \nSolid understanding of LLM architectures and their underlying mechanisms. \nFamiliarity with MLOps principles, CI/CD pipelines, Docker, and Kubernetes. \nUnderstanding of Khmer linguistics and script characteristics, including word segmentation and tokenization challenges. \nKnowledge of data structures, algorithms, and software engineering best practices. \nMinimum of 3+ years of hands-on experience in Natural Language Processing (NLP) or Machine Learning engineering. \nProven experience working with Large Language Models (LLMs), including pre-training, fine-tuning, or deployment. \nStrong proficiency in Python and relevant NLP/ML libraries \nExperience with cloud platforms for training and deploying ML models. \nExperience building and managing large-scale datasets and data pipelines. \nStrong analytical and problem-solving mindset, with attention to detail in experimentation and evaluation. \nClear communicator, able to explain complex technical concepts to both technical and non-technical audiences. \nHigh integrity in handling data, respecting privacy, licensing, and ethical considerations. \nIntellectual curiosity and self-motivation to keep pace with a fast-moving research field. \nCollaborative team player, able to work closely across teams and with stakeholders.","description_format":"text","description_chars":3035,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"Khmer","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commercial & Retail Banks","Money Transfer & Remittances"],"lifecycle":[{"event":"open","at":"2026-10-02T10:20:01Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":35,"reasons":["conf:3","win:early"],"computed_at":"2026-10-03T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/wing-bank-big-data-engineer","json_url":"https://alion.io/job/wing-bank-big-data-engineer.json","meta":{"generated_at":"2026-10-04T03:06:35Z","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":4724,"day_limit":5000,"remaining_today":276,"minute_limit":60,"resets_at":"2026-10-05T00:00:00Z"}}}