{"id":692143,"url":"https://alion.io/job/aubrant-genai-engineer","title":"GenAI Engineer","company":{"id":673309,"name":"Aubrant Digital","domain":"aubrant.com","url":"https://alion.io/company/aubrant","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":{"grade":"C","score":67,"open_postings":18,"ghost_share":0.556,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Escazú, Costa Rica"],"countries":["CR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Anthropic","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Azure","optional":false},{"name":"Copilot","optional":false},{"name":"Embeddings","optional":false},{"name":"Gemini","optional":false},{"name":"IAM","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OCR","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false}],"status":"live","first_seen_at":"2025-07-17T15:27:29Z","employer_posted_date":"2025-07-17","last_verified_at":"2026-10-18T00:23:07Z","board_verified":true,"closed_at":null,"days_open":457,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":456},"description":"About Aubrant\nAubrant Digital is a leader in multi-shore custom application development. We are passionate about solving our clients’ business problems through consultative teamwork, innovative software, and proven processes. We’ve served more than 50 clients and delivered hundreds of high quality, custom enterprise applications. Our clients value us as integral team members who get the job done on time and on spec, and we are proud of our high client retention rate and under 2% staff turnover. With offices in New Jersey, Boston, Costa Rica, and Eastern Europe, we execute the full software lifecycle, from architecture and design through development, QA and application maintenance & support. Our company culture emphasizes client service, trust-based relationships, and innovation.\n Overview:\nWe’re seeking an experienced strong Generative AI Engineer to support enterprise-wide AI enablement initiatives. This role will focus on building robust GenAI and agentic AI workflows, automating workflows and working across diverse platforms like AWS, Microsoft 365, MS Copilot, and other 3rdparty GenAI platforms and libraries.\nThe ideal candidate is highly self-driven and comfortable operating across architecture, and hands-on implementation. This is a high-impact role supporting an AI Center of Excellence (CoE) at scale.\nExperience:\nOver 10 years of robust software engineering experience, including 2 to 3 years of dedicated hands-on work in Generative AI and Retrieval-Augmented Generation (RAG) architecture \nOver 3 years of cloud native experience preferably on AWS. \nOver 5 years of hands-on experience with Python \nExperience building interoperable Agentic AI Proof of Concepts using Model Context Protocol (MCP) and/or Google A2A. \nKey Responsibilities:\nDesign and develop GenAI solutions using prompt engineering, Retrieval-Augmented Generation (RAG), and custom pipelines \nDesign and develop interoperable AI agents using Model Context Protocol (MCP) and Google A2A \nAutomate workflows involving parsing unstructured content such as emails, documents, and web pages in to highly accurate and reliable structured content \nAutomate building documents using data and content from various diverse sources \nBuild enterprise-wide reusable services and components \nDesign and build MCP hosts, clients and servers \nEstablish frameworks for automated LLM testing \nCreate regression test suites to detect drift or prompt breakage \nIntegrate with internal and external web services using secure authentication and authorization mechanisms \nAdopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines \nExperience with agile methodologies and ability to independently document user stories in the absence of Business Analyst \nCollaborate with the AI CoE team to ensure scalability, reusability, and alignment with governance standards \nRequired Skills:\nVery strong Python skills. \nStrong hands-on experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic, etc.) using Python and Python based frameworks \nStrong hands-on experience in prompt engineering, context construction, grounding strategies \nStrong hands-on experience with Retrieval Augmented Generation (RAG) extracting, chunking and create embeddings from unstructured documents from diverse sources including O365(email, word, excel), PDFs, and webpages. \nComfortable building Model Context Protocol (MCP) clients, servers and hosts. \nStrong Expertise in building REST APIs and integrating with internal/external APIs \nHands-on experience with Intelligent Document Processing and/or OCR technologies on complex documents \nKnowledge of Google A2A \nDeep experience in AWS (Lambda, Bedrock, Step Functions, API Gateway, IAM) \nStrong experience with monitoring using LangSmith, CloudWatch, or other similar GenAI observability tools \nExcellent GenAI foundations and concepts \nClear understanding of enterprise data privacy, AI governance, and observability \nNice to Have:\nExperience with Microsoft Copilot extensibility (Graph connectors, plugins, or adaptive cards) \nAbility to build and orchestrate AI-to-AI interactions using Google A2A, integrating multiple agents or tools \nStrong traditional ML Experience \nSuccess Profile:\nYou’re a builder who can translate fuzzy business needs into elegant GenAI and Agentic AI workflows \nYou’re a problem solver who’s comfortable navigating across cloud platforms, tools, and data sources \nYou’re collaborative, with the ability to work independently while aligning with enterprise AI goals","description_format":"text","description_chars":4570,"description_truncated":false,"requirements":{"experience_years_min":2,"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":["Custom Software Development"],"lifecycle":[{"event":"open","at":"2026-09-11T01:15:10Z"}],"visa":[],"liveness":{"score":2,"band":"cold","label":"Long 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