{"id":2070655,"url":"https://alion.io/job/saksoft-ai-full-stack-engineer-developer","title":"AI Full Stack Engineer Developer","company":{"id":3117890,"name":"Saksoft","domain":"saksoft.com","url":"https://alion.io/company/saksoft-com","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":6500,"max":9500,"currency":"SGD","period":"month","gross":true,"usd_annual":89052},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure DevOps","optional":false},{"name":"Celery","optional":false},{"name":"CI/CD","optional":false},{"name":"Confluence","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"Informatica","optional":false},{"name":"Java","optional":false},{"name":"Jenkins","optional":false},{"name":"Jira","optional":false},{"name":"Kubernetes","optional":false},{"name":"OpenShift","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Splunk","optional":false},{"name":"SQL","optional":false},{"name":"TypeScript","optional":false},{"name":"Azure","optional":true}],"status":"live","first_seen_at":"2026-10-08T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-10-08T00:00:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Experience: 6+ Years \nRole: AI Full Stack Engineer Developer\n\nKey Skills:\n· DevOps: CICD, Kubernetes/Openshift, Docker,Jenkins, Bash\n· Backend: Python, SQL, Celery, Gen AI prompting\n· Frontend: TypeScript, HTML, Jinja\n\nResponsibilities:\nWe are looking for a AI Full Stack Engineer who operates effectively at the intersection of system analysis, enterprise integration and data engineering to support GenAI initiatives.\nThe successful candidate will:\n· Understand business and functional requirements\n· Translate them into data flows and integration designs\n· Work across upstream and downstream systems\n· Ensure reliable movement, transformation and availability of data for GenAI use cases\n· Develop scripts / programs to get data from different integration systems\n· Develop APIs to integrated with relevant systems.\nThe focus is on getting data into the right place, in the right format, reliably.\nWhat Matters Most\n Strong system thinking and ability to understand end to end flows \n Ability to challenge bad designs and propose better ones \n Strong stakeholder communication \n Hands on enough to troubleshoot and implement, but not a pure coder \n Experience navigating messy enterprise ecosystems\nKey Responsibilities\n 1. System Analysis & Design\nAnalyse business/technical requirements and translate them into data flows and integration designs\nWork with upstream and downstream teams to define data contracts and interfaces\nIdentify gaps, inefficiencies and risks in current data movement processes\nPropose pragmatic solutions balancing speed, quality and maintainability\n2. Integration & Data Movement\nDesign and implement data movement across systems using: \nAPIs\nSFTP and file based transfers\nBatch pipelines\n\nCoordinate integrations across systems in the DataLake ecosystem (Informatica, Cloudera, etc.)\nEnsure data is correctly transformed, mapped and delivered to target systems\nTroubleshoot integration issues across environments\n3. Data Preparation for GenAI\nSupport data ingestion and preparation for GenAI use cases: \ndocument ingestion\ndata aggregation\nenrichment and transformation\n\nWork with structured and unstructured data\nEnsure data is usable for downstream AI workflows (RAG, search, investigation flows)\nYou are not asking them to build models, just make data usable for them.\n4. Delivery & Coordination\nWork across multiple teams: \ndata platforms\napplication teams\ninfrastructure\nsecurity\n\nSupport SIT, UAT and production rollouts\nEnsure integration reliability, error handling and monitoring\nDocument flows, mappings and interfaces clearly\n5. Production Support\n· Provide production support for deployed applications, including incident investigation, troubleshooting, resolution, and service restoration.\n· Perform bug fixes, code corrections, and application development changes required to resolve production issues and improve application stability.\n· Develop and implement enhancements or technical changes arising from production issues, operational requirements, or continuous improvement.\n· Troubleshoot issues across the full technology stack, including Python, Celery, database, Docker, Kubernetes/OpenShift, and CI/CD.\nRequirements:\nKey Requirements\nBelow are the key skillsets that will be required for all relevant tasks mentioned:\n· 5-10 years of experience in system analysis, integration engineering, data engineering or technical delivery roles.\n· Good SQL and Python skills for data handling, scripting, automation and troubleshooting.\n· Good knowledge of front end stack, such as HTML, Jinja templating.\n· Experience with Celery for asynchronous / background task processing\n· Strong ability to translate requirements into system flows, data flows, interface specifications and implementation plans.\n· Experience working with upstream and downstream teams to define and deliver enterprise integrations.\n· Practical experience with REST APIs, SFTP, batch processing, file based integration and data pipeline orchestration.\n· Good understanding of data mapping, transformation, aggregation, reconciliation and data quality controls.\n· Exposure to Java\n· Exposure to Cloudera or similar enterprise data platforms.\n· Working knowledge of Git, branching, pull requests, code reviews and controlled release practices.\n· Familiarity with CI/CD, Jira, Confluence and enterprise deployment processes.\n· Experience of deploying and supporting application on Kubernetes and OpenShift (OCP)\n· Experience with Control M or equivalent scheduling tools.\n· Familiarity with logging and monitoring tools such as Splunk, Elastic Stack.\n· Exposure to GenAI concepts such as document ingestion, RAG, embeddings and data preparation for AI workflows.\nStrong communication skills, with the ability to challenge weak designs and coordinate across business, application, data, infrastructure and security teams.\nSkills\nLiaising with cross functional teams, Ai, Basic HTML, Jenkins, Splunk, Kubernetes, scalable solutions, data-flow diagrams, Azure DevOps, TypeScript, Data Integration, Bash, Systems Analysis and Design, Data Migration, JIRA, Python, Rest Apis, Docker, SFTP, Data 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