{"id":3419244,"url":"https://alion.io/job/mathco-ai-architect","title":"AI Architect","company":{"id":455909,"name":"MathCo","domain":"mathco.com","url":"https://alion.io/company/mathco","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":32000,"max_usd":73000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AgentOps","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon S3","optional":false},{"name":"Angular","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AutoGen","optional":false},{"name":"AutoGPT","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"BentoML","optional":false},{"name":"C#","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"CrewAI","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"DynamoDB","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FinOps","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Go","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Grafana","optional":false},{"name":"GraphQL","optional":false},{"name":"gRPC","optional":false},{"name":"Hallucination","optional":false},{"name":"Haystack","optional":false},{"name":"HIPAA","optional":false},{"name":"Hugging Face","optional":false},{"name":"IAM","optional":false},{"name":"Jaeger","optional":false},{"name":"Java","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LDAP","optional":false},{"name":"Least Privilege","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"MLFlow","optional":false},{"name":"MongoDB","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Next.js","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Pinia","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pulumi","optional":false},{"name":"Python","optional":false},{"name":"RabbitMQ","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Redux","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Snyk","optional":false},{"name":"SOC 2","optional":false},{"name":"SonarQube","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Storybook","optional":false},{"name":"Swagger","optional":false},{"name":"Terraform","optional":false},{"name":"Transformers","optional":false},{"name":"TypeScript","optional":false},{"name":"Vault","optional":false},{"name":"Vite","optional":false},{"name":"Vue.js","optional":false},{"name":"Weaviate","optional":false},{"name":"Webpack","optional":false},{"name":"WebSockets","optional":false},{"name":"Zustand","optional":false},{"name":"JavaScript","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"React Native","optional":true},{"name":"React.js","optional":true},{"name":"Semantic Search","optional":true},{"name":"SLI/SLO/SLA","optional":true}],"status":"live","first_seen_at":"2026-10-28T20:27:23Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-30T03:07:11Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Job Description\nWe’re looking for a seasoned Software Architect with deep expertise in cloud-native enterprise systems and Generative AI. You will define and deliver scalable, secure, and production-grade GenAI architectures - including multi-agent, RAG, LLMOps and AgentOps systems - and lead cross-functional teams to build and operate them. This role combines hands-on technical leadership, systems thinking, and strong stakeholder management.\nKey responsibilities\nArchitecture & System Design\nDesign scalable, modular, and cloud-native architectures for GenAI applications(microservices, event-driven, serverless).\nDefine system boundaries, data flows, orchestration, and integration patterns forLLMs, vector DBs, embedding services, and tool integrations.\nProduce architecture artifacts ( Layered Architecture Diagrams, C4 Models, DFDs,Class, Sequence, ER & Use Case diagrams, different types of blueprints, APIcontracts, design and trade-off decisions).\nGenAI & Agentic Systems\nArchitect and deliver Retrieval-Augmented Generation (RAG) pipelines, NaturalLanguage to SQL Flows, fine-tuning strategies, multi-modal capabilities, and tool-augmented agents.\nDesign agent orchestration and multi-agent frameworks enabling planning,reasoning, and secure tool invocations, implement and design Agent prototypes andCommunication Protocols.\nDefine prompt engineering standards, memory models(episodic/semantic/procedural), and context management.\nLLMOps & AgentOps\nDefine and implement model lifecycle pipelines: training, fine-tuning, validation,deployment, rollback, and monitoring.\nBuild AgentOps processes for agent lifecycle, behavior tracking, governance andperformance optimization.\nAutomate CI/CD for models, agents and services (MLflow, TFX, BentoML, custompipelines).\nIntegration, Security & Compliance\nIntegrate GenAI services with enterprise systems (ERP, CRM, data lakes, APIs) usingsecure, scalable interfaces.\nEnsure secure access controls, data privacy, encryption, and compliance (GDPR,HIPAA, SOC2).\nDefine responsible AI practices: bias mitigation, explainability, audit trails, andoutput governance.\nGenAI security - classify, encrypt & sign data/models; enforce least-privilege withshort-lived creds and CI/CD security gates; telemetry, drift/hallucination alerts, kill-switch & runbooks.\nAgentic AI security - provable agent identity/attestation, tool allowlist + human gatefor high-risk actions; ephemeral scoped tokens, sandboxed execution,and replayable audit traces.\nObservability, Ops & Cost Optimization\nDefine telemetry, tracing, and logging for models and agents; monitor performance,drift, hallucination rates and user feedback loops.\nBuild dashboards, alerts and runbook guidance for operational health.\nDesign systems for cost efficiency (autoscaling, spot instances, serverlesschoices) and support FinOps practices.\nLeadership, Collaboration & Documentation\nLead cross-functional teams (product, data science, AI engineers, platform)through architecture reviews, workshops, and technical decisioning.\nMaintain architectural standards, documentation, playbooks, and patternlibraries for GenAI systems.\nMentor engineers and evangelize best practices across the organization.\nRequired qualifications & experience\n10+ years software engineering experience with 3+ years in architecture or seniortechnical leadership roles (or equivalent).\nProven track record designing and delivering cloud-native, production systems atenterprise scale.\nHands-on experience with GenAI/LLM systems, RAG, NL-SQL,agentic frameworks or similar productionized AI applications.\nStrong knowledge of system design patterns (microservices, event-driven,CQRS, hexagonal architecture), and Low Level Design Patterns.\nExperience integrating ML/LLM services with enterprise data platforms and APIswhile meeting security/compliance requirements.\nSolid engineering background in at least two languages (Python, TypeScript, Go,Java, C#) and familiarity with modern frameworks.\nTechnical skills & technologies (comprehensive)\nCloud & Infra: AWS / Azure / GCP; Kubernetes, Docker, serverless (Lambda, Functions, Cloud Run), GPU instances\nGenAI & ML: Hugging Face Transformers, OpenAI APIs, ,LangChain, LlamaIndex, Semantic Kernel, Haystack\nVector Stores: FAISS, Pinecone, Weaviate, Chroma, Postgres+pgVector, and other cloud vector stores\nLLMOps / MLOps: Custom Development of Ops Pipelines, MLflow, TFX, BentoML, Kubeflow\nData & Integration: Kafka, Spark, Airflow, Flink, ETL/ELT concepts, data lakes, API gateways (Apigee etc)\nDevOps & IaC: Terraform, Pulumi, CloudFormation, GitHub Actions, Jenkins\nObservability & Security: Prometheus, Grafana stack, OpenTelemetry, Jaeger, ELK, Datadog; Vault,\nIAM, LDAP/OAuth2/OIDC/SAML Connect, Snyk, SonarQube, SAST/SCA in pipelines, OWASPs, CWEs, CVEs.\nDatabases & Storage: Relational (RDS/Cloud SQL), NoSQL (Mongo, DynamoDB, Cosmos DB), Redis, S3/Blob/GCS, ORM/ODM frameworks.\nAgent frameworks / tools: Understanding of Basics of Agents required, Langgraph, Autogen, AutoGPT, AgentVerse, MetaGPT, CrewAI etc.\nPerformance & scalability: SSR/ISR, caching strategies (CDN, edge), lazy loading, bundle optimization, performance budgets.\nRealtime & asyncRealtime & async: WebSockets, SSE, message brokers (Kafka, RabbitMQ), background workers. Frontend frameworks: React (Next.js), Angular, Vue; component libraries and state (Redux/RTK, Context, Pinia, Zustand)\nStyling & UI tooling: Component Libraries, Accessibility best practices, Responsive UI\nFrontend build & tooling: Vite, Webpack, Storybook, UI Frameworks.\nBackend frameworks: Node.js/Express, FastAPI, serverless functions (AWS Lambda, Cloud Functions)\nAPI design & integration: REST, gRPC, OpenAPI/Swagger, API versioning and contract testing, GraphQL(Optional)\nUX & product mindset: Design-system familiarity, usability, accessibility, and working with designers\nBehavioral & leadership skills\nStrategic thinking with the ability to align architecture to product and businessgoals.\nExcellent communicator: simplify complex technical concepts for technical andnon-technical stakeholders.\nStrong mentorship skills - able to raise team capability in GenAI architectureand engineering.\nPragmatic decision-maker with a bias for measurable outcomes and trade-offanalysis.\nHigh attention to detail, ownership, and accountability for reliability, security,and cost.\nNice-to-have\nExperience operating LLMs in regulated industries (pharma).\nFamiliarity with prompt auditing, hallucination detection, and automated qualitychecks.\nBackground in knowledge engineering, semantic search, or knowledge graphs.\nAcademic background in CS, ML, or equivalent applied experience.\nMobile & cross-platform (optional): React Native, Flutter basics for mobileintegration\nDeliverables & success metrics (examples)\nProduction-ready GenAI architecture and deployment runbook.\nDeployed RAG/agent pipeline with observable SLOs and monitoring dashboards.\nReduced model hallucination/incidents and measurable improvement inretrieval quality.\nArchitecture decision records (ADRs), standards library, and cross-teamonboarding materials.\nCost targets achieved through optimized infra and autoscaling 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