Senior AI Architect

Role summary We’re hiring a Senior AI Architect to design and guide end-to-end AI solutions—from problem framing and model selection (LLMs/classical ML) to environment architecture, security, and cost performance. You’ll collaborate closely with a Solution Architect (overall system design/enterprise fit), while you own the AI architecture: which models, which vector/search technologies, which AWS services, how to integrate with data platforms, how to run safely and cost-effectively at scale. What you’ll do • Own AI architecture for initiatives (RAG, agents, predictive models, NLP, vision): define reference architectures, target state, and patterns (batch/real-time, online/offline inference). • Model selection & evaluation: choose LLMs/foundation models by use case (Bedrock models—Anthropic, Mistral, Meta, Cohere, etc.; SageMaker-hosted custom; open-source). Define evals (quality, latency, safety), guardrails, and fallback strategies. • AWS solution design (AI/ML stack): map use cases to services such as Amazon Bedrock, SageMaker (Studio/Training/Inference/Experiments/Model Registry), S3/Lake Formation, Kendra (or alternatives), OpenSearch, Lambda/Step Functions, EKS/ECS, API Gateway, CloudWatch/CloudTrail, ECR, Secrets Manager/SSM Parameter Store, KMS, MSK/Kinesis, Glue, Athena, Redshift, PrivateLink/VPC endpoints, WAF. • Vector & search tech choices: evaluate and standardize options (e.g., Kendra, OpenSearch vector, MongoDB Atlas Vector, pgvector, Pinecone, Weaviate) including ingestion, schema, embeddings, filters, TTL, and ops. • RAG/Agentic patterns: design retrieval pipelines (chunking, hybrid search, re-ranking), prompt orchestration, tool-use/function-calling, persona/policy layers, caching, and safety filters. • Environment planning: define dev/test/prod topologies, network isolation, data zones, GPU/accelerator strategy, CI/CD for models (MLOps) and prompts (PromptOps), blue/green or canary rollout for models. • Cost & performance engineering: produce FinOps projections and guardrails (token/throughput budgeting, autoscaling, spot strategy, quantization, distillation, response caching, batch vs. real-time tradeoffs). • Security, privacy, and governance: design guardrails for PHI/PII; encryption (at rest/in transit), row-level/column-level controls, key management, data retention; prompt-injection/EOP mitigations; model risk documentation. • ML platform collaboration: work with data scientists/ML engineers on feature stores, experiment tracking, offline/online parity, A/B tests, and evaluation pipelines. • Operational readiness: SLOs/SLIs, tracing and telemetry for prompts & models, incident playbooks, reliability and capacity planning. • Partnering & enablement: co-author solution docs with Solution Architect; create reference implementations, templates, and internal standards; mentor teams. Required qualifications • 8+ years in AI/ML and data engineering combined, with 3+ years as an architect for production AI systems at enterprise scale. • Proven delivery of LLM-powered apps (chatbots/agents/RAG) and classical ML services (forecasting, classification, ranking) in production. • Deep hands-on AWS experience across Bedrock and/or SageMaker plus core data/compute/networking (S3, Lake Formation, Glue, Redshift/Athena, Lambda/Step Functions, EKS/ECS, VPC, IAM, KMS). • Strong grasp of vector search and retrieval design, embeddings, re-ranking, and metadata filtering; experience with at least one managed vector/search option (Kendra/OpenSearch/MongoDB Atlas Vector/Pinecone). • Solid MLOps practices: model registries, CI/CD, automated evaluation, feature stores, model/package versioning, rollbacks, and observability. • Security & compliance literacy (IAM, tokenization, KMS, network isolation, audit), with experience in regulated data (e.g., PHI/PII). • Ability to build cost models and optimize end-to-end latency/throughput and spend (GPU sizing, autoscaling, caching, quantization). • Excellent architecture documentation, stakeholder communication, and leadership with cross-functional teams. Preferred / nice to have • Experience with Azure AI (Azure OpenAI, Cognitive Search, Synapse) for hybrid/multi-cloud considerations. • Experience with Snowflake Cortex/ICEBERG or Databricks MosaicML/Dolly ecosystems. • Familiarity with agent frameworks and tool-calling (e.g., LangChain, LlamaIndex) and productionizing them on AWS. • Hands-on with Kafka/MSK, event-driven patterns, streaming features/online inference. • FinOps Practitioner mindset; prior ownership of multi-million-token or GPU budgets. • Prior work in healthcare/financial services or other regulated industries; FedRAMP experience preferred. • Cloud or AI certifications (AWS ML Specialty, AWS Solutions Architect Pro, Azure Data/AI Engineer).