{"id":3600777,"url":"https://alion.io/job/saksoft-senior-genai-application-engineer-2","title":"Senior GenAI Application Engineer","company":{"id":3117890,"name":"Saksoft","domain":"saksoft.com","url":"https://alion.io/company/saksoft-com","size_band":"1001-5000","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":8000,"max":10000,"currency":"SGD","period":"month","gross":true,"usd_annual":93936},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"OpenShift","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Langfuse","optional":true},{"name":"Redis","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-11-02T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-11-02T00:00:00Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Experience: 6+ Years\nRole: Senior GenAI Application Engineer\n\nKey Skills:\n· Production GenAI Application Engineering\n· LangGraph, LangChain, RAG and Agentic Workflows\nPython, Java, APIs and Enterprise Integration\nResponsibilities:\nBuild and improve GenAI applications using frameworks such as LangGraph, LangChain or similar orchestration tools.\nWork with open weight models, hosted LLMs and model serving patterns.\nDevelop agentic workflows, retrieval augmented generation, tool calling and prompt orchestration.\nIntegrate GenAI applications with enterprise systems, APIs, data sources and operational platforms.\nEngineer solutions for production quality, including logging, tracing, evaluation, fallback behaviour and troubleshooting.\nBring a strong can do attitude, enthusiasm, curiosity and practical delivery mindset.\nStrong hands on experience building production grade GenAI applications.\nPractical experience with LangGraph, LangChain, agentic workflows, RAG and tool calling.\nUnderstanding of open weight models and how to integrate them into real applications.\nAbility to challenge weak designs and propose better ones.\nStrong engineering discipline around reliability, testing, observability and maintainability.\nAbility to work across application, data, platform, security and infrastructure teams.\nEnthusiastic, energetic and willing to get into the details.\nPragmatic approach to delivery, not over engineering, not hype driven.\nRequirements:\nBelow are the key skillsets required for the role:\n6 or more years of software engineering experience, preferably with recent hands on experience in GenAI application development.\nProven experience building production grade GenAI applications, not only prototypes, experiments or demos.\nStrong hands on experience with LangGraph, LangChain or similar orchestration frameworks.\nPractical understanding of RAG, agentic workflows, tool calling, prompt orchestration and context management.\nExperience integrating LLMs into real applications using APIs, backend services and enterprise data sources.\nExperience with open weight models or strong interest backed by hands on experimentation.\nStrong backend engineering skills using Python, Java or similar languages.\nGood understanding of API design, distributed systems and production resilience patterns.\nExperience with observability, logging, tracing and troubleshooting for GenAI or backend applications.\nFamiliarity with containerised deployment environments such as Kubernetes or OpenShift.\nAbility to write clean, maintainable and testable code.\nStrong analytical, debugging and troubleshooting skills.\nHigh ownership, high curiosity and genuine interest in building useful AI products.\nAbility to work in a fast moving environment with incomplete information and evolving requirements.\nStrong communication skills, with the ability to challenge weak designs and coordinate across business, application, data, infrastructure and security teams.\nNice to Have\nExperience with DeepAgent or similar agent frameworks.\nExperience with Langfuse, Elastic or similar observability and search tools.\nExperience with Redis for caching, conversation state, rate limiting, queue backed workflows or low latency GenAI application patterns.\nExperience with vLLM or similar inference serving frameworks for running open weight models.\nExperience deploying GenAI workloads on cloud or container platforms.\nSkills\nGentoo, Model Serving, Retrieval-Augmented Generation (RAG), Teamwork, Leadership, Kubernetes, Application Engineering, LangGraph, Workflow Development, LangChain, Application Frameworks, Python, Workflow Engine, Enterprise Integration, Java, Orchestration, API","description_format":"text","description_chars":3666,"description_truncated":false,"requirements":{"experience_years_min":6,"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":["IT Consulting & Digital Transformation","Custom Software 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