{"id":1507907,"url":"https://alion.io/job/unison-consulting-aiml-engineer","title":"AI/ML Engineer","company":{"id":1872987,"name":"Unison Consulting","domain":"unison-ucg.com","url":"https://alion.io/company/unison-ucg","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":"senior","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":10000,"max":12000,"currency":"SGD","period":"month","gross":true,"usd_annual":112680},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-30T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-09-30T00:00:00Z","board_verified":false,"closed_at":null,"days_open":11,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":11},"description":"About the Role\nWe are looking for an experienced AI/ML Engineer witha strong background in machine learning and recent hands-on experience in ArtificialIntelligence and Generative AI. The successful candidate will work onbuilding, deploying and supporting AI/ML solutions using the Databricksplatform, collaborating with data, technology and business stakeholdersacross the region.\nThis role is suited to someone who combines strong MLengineering fundamentals with practical experience in emerging AI technologiesand can translate business requirements into scalable AI solutions.\nKey Responsibilities\nDesign, develop and deploy scalable Machine Learning and AI solutions using Databricks.\nDevelop and productionise ML models across the end-to-end lifecycle, including data preparation, feature engineering, model training, evaluation and deployment.\nWork with Generative AI / LLM technologies to develop practical AI use cases and solutions.\nBuild solutions involving RAG, embeddings, vector search, prompt engineering and AI/LLM applications where applicable.\nDevelop and optimise data and ML pipelines using Python, PySpark and SQL.\nUtilise MLflow and Databricks ML/AI capabilities for experiment tracking, model management and deployment.\nCollaborate with Data Engineers, Data Scientists, Architects and business stakeholders to deliver production-ready solutions.\nImplement appropriate practices around model monitoring, performance, scalability, security and responsible AI.\nStay current with emerging developments in AI, GenAI and Machine Learning and assess their applicability to business requirements.\nSupport technical discussions and communicate AI/ML concepts effectively to both technical and non-technical stakeholders.\nWork with regional and international teams and stakeholders in a collaborative environment.\nRequired Skills & Experience\nBachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering or a related discipline.\n5+ years of experience in Machine Learning, Data Science or AI/ML Engineering.\nStrong hands-on experience with Python and SQL.\nStrong experience with Databricks and modern data/ML platforms.\nPractical experience developing and deploying Machine Learning models in production environments.\nRecent hands-on experience with AI / Generative AI / LLM technologies.\nUnderstanding of ML lifecycle, MLOps, model deployment and monitoring.\nExperience with MLflow or equivalent ML lifecycle management platforms.\nGood understanding of data processing frameworks such as PySpark.\nExperience with cloud platforms such as AWS, Azure or GCP.\nSkills\nMachine Learning, Ai, Vector Search, Model Driven Development, implementing monitoring tools, Artificial Intelligence, Azure Machine Learning, LLMOps, Model Training, Databricks, Python, Artificial Intelligence Application, Generative AI, Prompt 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