Role Overview
We 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.
Working 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.
Key Responsibilities
- Define and drive the architecture, technical roadmap, and engineering standards for WorkSpan's AI platform.
- Own the architecture and delivery of Agentic AI solutions, including agents, tools, memory, orchestration, and execution frameworks.
- Design and implement multi-agent patterns including parallel execution, tool routing, agent handoffs, and workflow orchestration.
- Architect enterprise-scale RAG systems combining structured and unstructured data sources.
- Define retrieval strategies including embeddings, chunking, hybrid search, reranking, and grounding techniques.
- Design query orchestration systems that translate natural language into structured queries and business insights.
- Architect scalable AI services supporting model orchestration, routing, authentication, and real-time streaming responses.
- Define performance, scalability, reliability, and cost optimization strategies for AI workloads.
- Architect AI infrastructure across AWS and GCP environments, including deployment, observability, and operational best practices.
- Establish prompt lifecycle management standards including versioning, governance, rollout, and promotion workflows.
- Drive LLMOps practices covering experimentation, evaluation, benchmarking, monitoring, and regression testing.
- Build evaluation frameworks to measure retrieval quality, answer relevance, faithfulness, and overall AI performance.
- Define observability standards including tracing, telemetry, performance monitoring, and operational health metrics.
- Establish AI security, guardrails, privacy, governance, and responsible AI standards, including prompt injection protection, secure tool invocation, data protection, and policy enforcement.
- Lead architecture reviews, technology evaluations, and build-versus-buy decisions.
- Mentor engineers and architects while driving adoption of reusable design patterns and engineering best practices.
- Partner with business and technology leaders to translate strategic objectives into scalable AI solutions.
Required Qualifications
Experience & Education
- 10+ years of software engineering experience with expertise in distributed systems and cloud-native applications.
- 3+ years designing, leading, or architecting AI/ML/Agentic platforms and large-scale AI initiatives.
- Proven experience delivering production AI systems in enterprise SaaS environments.
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related technical field.
Cloud Platforms
- Hands-on experience deploying and operating AI workloads on AWS or GCP at production scale.
- Experience with cloud-native architectures, container platforms, serverless computing, and managed AI services.
- Strong understanding of cloud security, identity management, secrets management, and least-privilege access controls.
- Experience building scalable, secure, and highly available enterprise platforms.
AI / ML Frameworks & Techniques
- Deep expertise in Agentic AI, RAG architectures, LLM orchestration, and prompt engineering.
- Experience with multi-agent systems, tool calling, memory management, and workflow orchestration.
- Strong understanding of embeddings, vector search, hybrid retrieval, reranking, and grounding techniques.
- Experience designing query orchestration systems that leverage LLMs to interact with enterprise data.
- Knowledge of model optimization, evaluation frameworks, and inference performance tuning.
- Experience implementing AI governance, observability, quality evaluation, guardrails, and security controls for enterprise AI systems.
Prompt Lifecycle & LLMOps
- Experience with prompt registries, versioning systems, governance workflows, and environment-based promotions.
- Experience with experimentation, evaluation, benchmarking, regression testing, and production monitoring.
- Familiarity with model routing, tenant-specific AI configurations, and prompt performance optimization.
Programming & Integration
- Python (Primary): Experience building production AI services, orchestration layers, APIs, agent runtimes, and data pipelines.
- Java / Spring Boot: Experience integrating AI capabilities into enterprise applications and distributed systems.
- Strong SQL and PostgreSQL expertise.
- Experience designing event-driven and service-oriented architectures.
Preferred Technologies
Experience with one or more of the following technologies:
Agentic AI & Orchestration: Strands, CrewAI, LangGraph, LangChain, LlamaIndex, OpenAI Agents SDK
Cloud & Platform: Amazon Bedrock, Vertex AI, Kubernetes, Docker, Terraform, Kafka
LLMOps & Observability: MLflow, Weights & Biases, OpenTelemetry, Prometheus, Grafana
Vector Search: pgvector, OpenSearch, Pinecone, Weaviate
Nice to Have
- Infrastructure-as-Code proficiency using Terraform, CDK, or similar technologies.
- Experience deploying and operating AI workloads using Docker and Kubernetes.
- Strong understanding of observability, distributed tracing, metrics, monitoring, and reliability engineering.
- Experience implementing secure authentication, authorization, secrets management, auditability, and data protection controls for AI systems.
- Experience with B2B SaaS multi-tenant architectures and per-customer AI customization at scale.
- Background in partner ecosystem management, CRM/PRM platforms, or workflow automation platforms.
- Contributions to open-source AI projects, agent frameworks, or published research.
- Familiarity with multimodal AI, document intelligence, or knowledge extraction systems.
- Experience with federated or on-premise AI deployments for enterprise customers.
- Prior experience scaling AI platforms from 0 to 1 in a high-growth environment.
Key Skills
Agentic AI
RAG Pipelines
LLM Orchestration
Streaming Inference
Prompt Registry
LLMOps
Python
Java / Spring
Multi-Cloud
Vector Search
Infrastructure as Code
Observability
Multi-Agent Systems
Query Orchestration
Embedding Models
PostgreSQL
AI Security
Responsible AI