MLOps Architect - Gen Al

Kapitus is a trusted name in small business financing, seeking a senior MLOps Architect to design and scale a modern ML and Generative AI platform across AWS. The role focuses on owning the architecture for ML and Generative AI pipelines, ensuring production reliability, governance, cost optimization, and enterprise-grade security.

Responsibilities:

• Design and implement scalable ML and LLM infrastructure on AWS (SageMaker, EKS, S3, IAM, Lambda, Step Functions, CloudWatch)
• Architect end-to-end ML and Generative AI lifecycle workflows: Data ingestion & preprocessing, Feature engineering / embedding generation, Model training & fine-tuning (traditional ML + foundation models), Model evaluation & validation, Deployment (real-time, batch, streaming), Monitoring & retraining
• Integrate LLM pipelines (prompt workflows, RAG architectures, fine-tuning flows) into the enterprise MLOps stack
• Define standards for CI/CD/CT pipelines across ML and GenAI workloads
• Architect Retrieval-Augmented Generation (RAG) pipelines including: Embedding generation workflows, Vector database integration, Document ingestion and chunking strategies, Retrieval evaluation and monitoring
• Design and deploy LLM-based services using: Managed services (e.g., SageMaker endpoints, Bedrock-style APIs), Containerized custom inference services
• Establish prompt versioning, evaluation frameworks, and experiment tracking for LLM systems
• Implement guardrails for hallucination control, safety monitoring, bias detection, and usage logging
• Define architecture for LLM fine-tuning workflows (including data curation, evaluation, and cost controls)
• Implement scalable orchestration of LLM pipelines using workflow engines and event-driven patterns
• Architect scalable inference patterns for: Traditional ML models, LLM APIs, RAG systems
• Implement model monitoring frameworks for: Performance degradation, Drift detection, LLM output quality, Latency and token usage metrics
• Define SLAs/SLOs for ML and GenAI systems
• Design safe deployment strategies (blue/green, canary, shadow testing)
• Establish logging, observability, and traceability standards for GenAI systems
• Implement cost tracking for: Training workloads, GPU utilization, Inference endpoints, Token consumption (LLM APIs), Vector database storage
• Optimize LLM workloads for cost-performance tradeoffs (model size, batching, caching strategies)
• Design autoscaling and compute optimization strategies for GPU and CPU-based inference
• Partner with finance and engineering teams to forecast ML/GenAI infrastructure spend
• Define enterprise standards for: Experiment tracking, Model registry, Prompt registry, Artifact management, Embedding versioning
• Provide architectural guidance to data science, AI, and engineering teams
• Evaluate and recommend tooling across the ML/GenAI stack (MLflow, feature stores, vector databases, orchestration tools)
• Drive documentation and reusable patterns for ML and GenAI development