{"id":945530,"url":"https://alion.io/job/quantiphi-technical-architect-ml-genai-2","title":"Technical Architect - ML - GenAI","company":{"id":6155,"name":"Quantiphi","domain":"quantiphi.com","url":"https://alion.io/company/quantiphi","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":72,"open_postings":65,"ghost_share":0.462,"stale_share":0,"repost_share":0.015,"time_to_fill_p50_days":34,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":167000,"max_usd":315000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":197},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"BERT","optional":false},{"name":"Claude","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Interpretability","optional":false},{"name":"Kubeflow","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"Tokenization","optional":false},{"name":"Transformers","optional":false},{"name":"CI/CD","optional":true},{"name":"NER","optional":true},{"name":"NLP","optional":true}],"status":"live","first_seen_at":"2026-09-15T20:31:10Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-11-01T20:51:45Z","board_verified":true,"closed_at":null,"days_open":47,"trust":{"level":"ok","repost_count":1,"flags":["company_stale"],"days_open":46},"description":"While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.\nIf working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!\nRole: Gen AI Architect (AWS)\nExperience Level: 8+ Years\nWork location: Remote (US)\nJob Overview:\nWe are looking for a Generative AI Architect / Lead to design and deliver enterprise-grade GenAI solutions using AWS Bedrock and Agentcore. This role focuses on building scalable applications leveraging large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI workflows.\nThe ideal candidate will be a hands-on architect who can define solution architecture, guide teams, and actively contribute to development while ensuring performance, scalability, and cost efficiency.\nKey Responsibilities:\nDesign and implement GenAI solutions using AWS Bedrock and Agentcore\n\nDefine architecture for LLM-based applications, including RAG pipelines and agentic workflows\n\nDevelop and orchestrate agentic AI workflows, enabling multi-step reasoning, tool usage, and task automation\n\nBuild and manage RAG pipelines, including embeddings, retrieval mechanisms, and vector databases\n\nIntegrate LLM capabilities into enterprise applications via APIs and backend services\n\nDesign and optimize prompt engineering strategies for accuracy, relevance, and performance\n\nWork with structured and unstructured data sources to enable knowledge-driven AI applications\n\nEnsure model evaluation, monitoring, and optimization for latency, cost, and response quality\n\nCollaborate with application, data, and platform teams for end-to-end solution delivery\n\nDefine best practices for security, governance, and responsible AI usage\n\nTroubleshoot and resolve issues in production GenAI systems\n\nProvide technical leadership and mentor team members while remaining hands-on\n\nMust have:\n8+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.\n\nHands-on experience on AWS services. Proven experience using AWS Sagemaker and Bedrock leveraging different types of data sources, Training jobs, real-time and batch applications.\n\nDesign and implement agentic AI architectures using frameworks such as LangChain, Strand Agents etc., enabling autonomous task planning, decision-making, and multi-step reasoning.\n\nHands-on experience with Amazon AgentCore for building, deploying, and scaling production-grade agentic AI applications, including agent memory management, tool registry, and observability.\n\nArchitect and deploy scalable AI solutions on AWS, leveraging services like Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.\n\nProficiency in working with LLM APIs (e.g., Claude, Nova, and other third-party LLM providers), including API integration,and multi-model orchestration strategies.\n\nHands-on experience fine-tuning or optimizing large language models (LLM)\n\nFamiliarity with LLM tool use, prompt templating and context management.\n\nStrong expertise in Vector Databases, including indexing strategies, embedding generation, similarity search, and integration with RAG architectures.\n\nModel Evaluation & Optimization: Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.\n\nDevelop and maintain Model Context Protocol (MCP) implementations to manage state, context windows, memory, and prompt orchestration across distributed agent systems.\n\nExperience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.\n\nExperience implementing secure, scalable APIs and integrating with 3rd-party data sources and tools\n\nAbility to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.\n\n[redacted]\n\n\nExperience with software development, exposure to frontend backend frameworks and communication protocols\n\nExperience working on Infrastructure as Code (IaC) and CI/CD pipelines\n\nExperience with NLP concepts: syntactic/semantic analysis, NER etc. \n\nIf you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!","description_format":"text","description_chars":4510,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Document AI","Cloud Consulting & Migration","Data Engineering & Migration 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