{"id":3926634,"url":"https://alion.io/job/tachyon-technologies-full-stack-ai-engineer","title":"Full Stack AI Engineer","company":{"id":4524376,"name":"Tachyon Technologies","domain":"tachyontech.com","url":"https://alion.io/company/tachyon-technologies","size_band":"501-1000","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":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Snowflake","optional":false},{"name":"Embeddings","optional":true},{"name":"Flask","optional":true},{"name":"Hallucination","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"JavaScript","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Next.js","optional":true},{"name":"React.js","optional":true},{"name":"Structured Outputs","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-11-07T19:35:00Z","employer_posted_date":null,"last_verified_at":"2026-11-07T19:35:00Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Job description:\n\nWe are looking for a Full Stack AI Engineer to help build and scale ciATHENA, CustomerInsights.AI's agentic AI platform for life sciences.\n\nThis is a hands-on product engineering role for someone who can work across the stack - from modern web applications and APIs to LLM-powered workflows, agent orchestration, enterprise data integration, and production deployment.\n\nYou will work closely with product managers, AI engineers, data engineers, architects, and domain experts to convert complex commercial life sciences workflows into intuitive, production-grade AI applications.\n\nWe are looking for someone comfortable operating in a 0-to-1 product environment, where requirements evolve quickly and engineers are expected to take ownership from problem definition through deployment.\n\nKey Responsibilities:\nDesign and build production-grade AI applications using modern frontend, backend, and GenAI technologies.\nDevelop intuitive user experiences for conversational AI, analytics, agent workflows, and decision-support applications.\nBuild scalable backend APIs and services for AI-powered applications using Python-based frameworks such as FastAPI.\nIntegrate LLMs and AI models from providers such as Anthropic, OpenAI, Azure OpenAI, and open-source models.\nBuild and integrate RAG pipelines, knowledge retrieval, semantic search, tool calling, and multi-agent workflows.\nDevelop capabilities that allow agents to interact securely with enterprise data, APIs, applications, and analytical workflows.\nWork with structured and unstructured data across Snowflake, Databricks, relational databases, object stores, and vector databases.\nImplement conversational and agentic workflows that can reason across data, documents, business rules, and domain-specific knowledge.\nDevelop reusable UI components and product frameworks for multiple ciATHENA applications and use cases.\nImplement authentication, authorization, SSO, RBAC, audit logging, and enterprise security controls.\nBuild AI observability, evaluation, tracing, monitoring, and feedback mechanisms.\nOptimize AI applications for performance, reliability, latency, and cost.\nContainerize and deploy services using Docker and cloud-native technologies.\nParticipate in architecture decisions, code reviews, testing, CI/CD, and production support.\nWork closely with Forward-Deployed AI Engineers to rapidly configure and deploy customer-specific use cases.\nTranslate product requirements and business workflows into working software with minimal handoffs.\n\nCore Technology Stack:\nFrontendReact\nNext.js\nTypeScript / JavaScript\nModern component libraries\nResponsive enterprise application design\nData visualization\n\nBackendPython\nFastAPI / Flask / Django\nREST APIs\nAsync programming\nMicroservices architecture\n\nAI / GenAIClaude / Anthropic APIs\nOpenAI / Azure OpenAI\nPrompt engineering\nTool/function calling\nRetrieval-Augmented Generation\nAgentic workflows\nMulti-agent systems\nStructured outputs\nEmbeddings and semantic search\n\nAI FrameworksLangGraph / LangChain\nLlamaIndex or equivalent frameworks\nModel Context Protocol (MCP) familiarity is a plus\n\nDataSQL\nPostgreSQL / Azure SQL\nSnowflake\nDatabricks\nVector databases\nObject storage\nData APIs and semantic layers\n\nCloud / DevOpsAzure preferred\nDocker\nKubernetes / Azure Container Apps\nGitHub Actions / CI/CD\nTerraform exposure is a plus\nLogging, monitoring, and observability\n\nWhat We're Looking For:\n4-6 years of software engineering experience with strong full-stack development capability.\nAt least 1-2 years of hands-on experience building applications using LLMs or GenAI technologies.\nStrong Python development skills.\nStrong experience with React, Next.js, TypeScript, or equivalent modern frontend frameworks.\nExperience building APIs, backend services, and distributed applications.\nHands-on experience integrating LLMs into production applications.\nUnderstanding of RAG, embeddings, vector search, prompt design, and agent architectures.\nAbility to work with enterprise data platforms and relational databases.\nStrong understanding of software engineering fundamentals, design patterns, testing, and maintainability.\nExperience building secure enterprise applications with authentication and authorization.\nAbility to independently take a feature from concept to production.\nComfort working in a fast-moving startup/product environment with evolving requirements.\nStrong problem-solving ability and willingness to work across traditional frontend, backend, data, and AI boundaries.\n\nStrongly Preferred:\nExperience building agentic AI products, not just chatbots.\nExperience with LangGraph or similar orchestration frameworks.\nExperience building enterprise SaaS or AI products from 0 to 1.\nExperience integrating AI applications with Snowflake or Databricks.\nFamiliarity with semantic layers, metadata catalogs, knowledge graphs, or enterprise search.\nExperience with AI evaluation frameworks, tracing, observability, and hallucination mitigation.\nExperience implementing human-in-the-loop workflows.\nExperience with structured output validation and deterministic AI workflows.\nExperience with enterprise security, auditability, and governance requirements.\nStartup or product-company experience where engineers owned features end-to-end.\n\nBonus:\nExperience in life sciences, healthcare, commercial analytics, or pharmaceutical technology.\nUnderstanding of pharmaceutical datasets such as claims, sales, HCP/HCO, payer, patient, market access, or field-force data.\nExperience with enterprise AI platforms or commercial analytics applications.\nContributions to open-source AI projects or demonstrated personal AI projects.\n\nProfile We Are Looking For:\n\nThis is not a role focused on building another generic agent or chatbot.\n\nciATHENA is being built as a hyper-verticalized agentic AI platform for life sciences, where agents need to understand domain context, interact with governed enterprise data, execute analytical workflows, collaborate with other agents, and produce auditable business decisions.\n\nYou will help build capabilities spanning: User Experience -> Agents -> Knowledge -> Data -> Analytics -> Enterprise Systems.\n\nWe therefore value engineers who can think beyond an individual technology layer and understand how the complete product works.\nSkills\nGen AI, Generative AI, 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