{"id":3298487,"url":"https://alion.io/job/cinter-networks-ai-engineer-dsml-cinter-career","title":"AI Engineer - DS&ML - Cinter Career","company":{"id":4419351,"name":"Cinter Networks","domain":"cinter.net","url":"https://alion.io/company/cinter","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"JazzHR","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United 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No\n・Working Hours: Standard Business Hours (CST)\n・Language: English Fluent\n•Key Responsibilities\nDesign, build, evaluate, and operationalize enterprise-grade AI and Generative AI solutions supporting the DS/ML AI Accelerator.\nPartner with data owners and business stakeholders to onboard approved documents, knowledge assets, and data sources into governed AI knowledge repositories.\nDevelop Retrieval-Augmented Generation (RAG) pipelines, metadata strategies, tagging frameworks, retrieval mechanisms, and source citation capabilities.\nCreate evaluation datasets and retrieval quality metrics to assess relevance, grounding, accuracy, and completeness of AI-generated outputs.\nEnsure generated content clearly distinguishes factual information, assumptions, inferences, and areas requiring SME validation.\nDesign and implement secure multi-agent AI workflows leveraging LLMs, retrieval systems, APIs, and deterministic processes.\nDefine agent roles, workflow orchestration, tool integrations, validation rules, error handling, and human-in-the-loop review processes.\nDevelop reusable prompts, workflow patterns, and orchestration frameworks to improve scalability, governance, and user adoption.\nEstablish technical and business-oriented AI evaluation metrics, including correctness, traceability, latency, user effort, and operational cost.\nParticipate in Agile development processes, sprint planning, backlog refinement, and solution demonstrations.\nImplement Responsible AI, security, privacy, data governance, and enterprise compliance requirements.\nCreate maintainable code, operational documentation, monitoring strategies, and knowledge refresh processes for long-term sustainability.\n•The Right Candidate Will PossessStrong hands-on experience developing and deploying LLM-enabled applications in production environments.\nDeep understanding of RAG architectures, embeddings, vector databases, search technologies, and knowledge management.\nExperience designing AI agent workflows and orchestrating tools, workflows, and APIs.\nStrong software engineering background with modern development best practices and collaborative delivery models.\nAbility to transform ambiguous business requirements into measurable and scalable AI solutions.\nStrong analytical mindset with the ability to evaluate AI quality, reliability, and business value.\nExcellent communication skills and ability to collaborate effectively with technical and non-technical stakeholders.\nCommitment to responsible AI, governance, security, privacy, and compliance standards.\n•Required Qualifications & SkillsBachelor's degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field, or equivalent practical experience.\n5+ years of experience in Software Engineering, Machine Learning Engineering, Data Science, AI Engineering, or related technical disciplines.\nHands-on experience building LLM-powered applications, AI assistants, RAG solutions, or agentic AI systems.\nStrong Python development experience with production-quality coding practices.\nExperience with APIs, structured tool integrations, testing frameworks, code reviews, CI/CD, observability, and software documentation.\nKnowledge of embeddings, vector databases, semantic search, retrieval systems, and AI evaluation methodologies.\nExperience defining and measuring AI quality metrics, retrieval effectiveness, and user outcomes.\nAbility to communicate technical concepts and tradeoffs to both business and technical stakeholders.\nExperience working within security, privacy, governance, and Responsible AI frameworks.\nExperience working in Agile development environments and delivering solutions in iterative sprint cycles.\n•Preferred QualificationsExperience with AI orchestration and agent frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or similar platforms.\nExperience implementing LLM evaluation frameworks, guardrails, prompt management, telemetry, and model risk controls.\nExperience supporting ML lifecycle management, model development, feature documentation, and AI product delivery.\nKnowledge of enterprise data governance, auditability, access controls, and human-in-the-loop validation processes.\nExperience working with Azure OpenAI, OpenAI, Anthropic, Gemini, AWS AI, or comparable enterprise AI platforms.\nExperience deploying AI solutions within regulated or large enterprise environments.\nNo C2C: We do not accept Corp-to-Corp (C2C) arrangements for this position.","description_format":"text","description_chars":4755,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"Japanese","level":"Advanced (C1)","optional":false},{"language":"English","level":"Advanced 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