{"id":731665,"url":"https://alion.io/job/workspan-ai-architect","title":"AI Architect","company":{"id":687650,"name":"Workspan","domain":"workspan.com","url":"https://alion.io/company/workspan-2","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":{"grade":"A","score":95,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":101,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":34000,"max_usd":79000,"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":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"CrewAI","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Grafana","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Least Privilege","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"OpenSearch","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"PagerDuty","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Spring Boot","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"Weights & Biases","optional":false},{"name":"Multimodal AI","optional":true}],"status":"live","first_seen_at":"2026-06-23T14:03:52Z","employer_posted_date":"2026-06-23","last_verified_at":"2026-11-05T00:42:53Z","board_verified":true,"closed_at":null,"days_open":134,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":133},"description":"About WorkSpan\nThe next era of growth is being driven by business interoperability. Cloud, genAI, solutions combining services and software- more and more, companies outpace their competition not just through building superior products, but by creating stronger partnerships, paths to market, and better business models for winning together. Cloud providers, service providers, tech partners and resellers are teaming up to win more deals together through co-selling.\nWorkSpan is building the world’s largest, trusted co-selling network.\nWorkSpan already has seven of the world’s ten largest partner ecosystems on our platform and $50B of customer pipeline under active management. AWS, Google, Microsoft, MongoDB, PagerDuty, Databricks and dozens of others trust WorkSpan to accelerate and amplify their ecosystem strategies.\nWith a $30M series C and backing from world class investors Insight Partners, Mayfield, and M12, WorkSpan is poised to drive the future of B2B. Come be a part of it.\nJoin our team for the opportunity to:\nOwn your results and make a tangible impact on the business\nDevelop a deep understanding of GTM working closely with leadership across sales & marketing\nWork with driven, passionate people every day\nBe a part of an ambitious, supportive team on a mission\nRole Overview\nWe are seeking an experienced Principal AI Architect to lead the design and evolution of WorkSpan's AI platform. You will own the end-to-end architecture of our AI systems-from agent orchestration and RAG pipelines to streaming inference, prompt lifecycle management, AI observability, and security.\nWorking closely with Product, Data Science, Platform Engineering, and Executive Leadership, you will define AI strategy, establish architecture standards, and build enterprise-grade AI capabilities. The ideal candidate combines deep expertise in AI/ML systems, cloud-native architecture, distributed systems, and enterprise software development, with a proven track record of delivering production AI platforms at scale.\nKey Responsibilities\nDefine and drive the architecture, technical roadmap, and engineering standards for WorkSpan's AI platform.\nOwn the architecture and delivery of Agentic AI solutions, including agents, tools, memory, orchestration, and execution frameworks.\nDesign and implement multi-agent patterns including parallel execution, tool routing, agent handoffs, and workflow orchestration.\nArchitect enterprise-scale RAG systems combining structured and unstructured data sources.\nDefine retrieval strategies including embeddings, chunking, hybrid search, reranking, and grounding techniques.\nDesign query orchestration systems that translate natural language into structured queries and business insights.\nArchitect scalable AI services supporting model orchestration, routing, authentication, and real-time streaming responses.\nDefine performance, scalability, reliability, and cost optimization strategies for AI workloads.\nArchitect AI infrastructure across AWS and GCP environments, including deployment, observability, and operational best practices.\nEstablish prompt lifecycle management standards including versioning, governance, rollout, and promotion workflows.\nDrive LLMOps practices covering experimentation, evaluation, benchmarking, monitoring, and regression testing.\nBuild evaluation frameworks to measure retrieval quality, answer relevance, faithfulness, and overall AI performance.\nDefine observability standards including tracing, telemetry, performance monitoring, and operational health metrics.\nEstablish AI security, guardrails, privacy, governance, and responsible AI standards, including prompt injection protection, secure tool invocation, data protection, and policy enforcement.\nLead architecture reviews, technology evaluations, and build-versus-buy decisions.\nMentor engineers and architects while driving adoption of reusable design patterns and engineering best practices.\nPartner with business and technology leaders to translate strategic objectives into scalable AI solutions.\nRequired Qualifications\nExperience & Education\n10+ years of software engineering experience with expertise in distributed systems and cloud-native applications.\n3+ years designing, leading, or architecting AI/ML/Agentic platforms and large-scale AI initiatives.\nProven experience delivering production AI systems in enterprise SaaS environments.\nBachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related technical field.\nCloud Platforms\nHands-on experience deploying and operating AI workloads on AWS or GCP at production scale.\nExperience with cloud-native architectures, container platforms, serverless computing, and managed AI services.\nStrong understanding of cloud security, identity management, secrets management, and least-privilege access controls.\nExperience building scalable, secure, and highly available enterprise platforms.\nAI / ML Frameworks & Techniques\nDeep expertise in Agentic AI, RAG architectures, LLM orchestration, and prompt engineering.\nExperience with multi-agent systems, tool calling, memory management, and workflow orchestration.\nStrong understanding of embeddings, vector search, hybrid retrieval, reranking, and grounding techniques.\nExperience designing query orchestration systems that leverage LLMs to interact with enterprise data.\nKnowledge of model optimization, evaluation frameworks, and inference performance tuning.\nExperience implementing AI governance, observability, quality evaluation, guardrails, and security controls for enterprise AI systems.\nPrompt Lifecycle & LLMOps\nExperience with prompt registries, versioning systems, governance workflows, and environment-based promotions.\nExperience with experimentation, evaluation, benchmarking, regression testing, and production monitoring.\nFamiliarity with model routing, tenant-specific AI configurations, and prompt performance optimization.\nProgramming & Integration\nPython (Primary): Experience building production AI services, orchestration layers, APIs, agent runtimes, and data pipelines.\nJava / Spring Boot: Experience integrating AI capabilities into enterprise applications and distributed systems.\nStrong SQL and PostgreSQL expertise.\nExperience designing event-driven and service-oriented architectures.\nPreferred Technologies\nExperience with one or more of the following technologies:\nAgentic AI & Orchestration: Strands, CrewAI, LangGraph, LangChain, LlamaIndex, OpenAI Agents SDK\nCloud & Platform: Amazon Bedrock, Vertex AI, Kubernetes, Docker, Terraform, Kafka\nLLMOps & Observability: MLflow, Weights & Biases, OpenTelemetry, Prometheus, Grafana\nVector Search: pgvector, OpenSearch, Pinecone, Weaviate\nNice to Have\nInfrastructure-as-Code proficiency using Terraform, CDK, or similar technologies.\nExperience deploying and operating AI workloads using Docker and Kubernetes.\nStrong understanding of observability, distributed tracing, metrics, monitoring, and reliability engineering.\nExperience implementing secure authentication, authorization, secrets management, auditability, and data protection controls for AI systems.\nExperience with B2B SaaS multi-tenant architectures and per-customer AI customization at scale.\nBackground in partner ecosystem management, CRM/PRM platforms, or workflow automation platforms.\nContributions to open-source AI projects, agent frameworks, or published research.\nFamiliarity with multimodal AI, document intelligence, or knowledge extraction systems.\nExperience with federated or on-premise AI deployments for enterprise customers.\nPrior experience scaling AI platforms from 0 to 1 in a high-growth environment.\nKey Skills\nAgentic AI\nRAG Pipelines\nLLM Orchestration\nStreaming Inference\nPrompt Registry\nLLMOps\nPython\nJava / Spring\nMulti-Cloud\nVector Search\nInfrastructure as Code\nObservability\nMulti-Agent Systems\nQuery Orchestration\nEmbedding Models\nPostgreSQL\nAI Security\nResponsible AI","description_format":"text","description_chars":7908,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Sales 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