MLOps Architect

Kapitus; Virginia (MN); On-site; USD 180,000 - 240,000; Full time

Job summary

Kapitus is seeking a Senior MLOps Architect to design and scale a modern ML and Generative AI platform across AWS. You will own the architecture for traditional ML and LLM pipelines, ensuring production reliability, governance, cost optimization, and enterprise-grade security in a large-scale environment.

You will lead end-to-end ML/GenAI workflows, integrate RAG pipelines and vector databases, define CI/CD standards, and enable secure deployment, monitoring, and retraining of models using

Qualifications

• 6+ years of experience in ML engineering, data engineering, or MLOps roles.
• Proven experience architecting ML platforms in AWS.
• Strong hands-on experience with SageMaker (training, pipelines, deployment).

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, feature engineering, model training, evaluation, deployment).
• Integrate LLM pipelines (RAG, prompts) into the enterprise MLOps stack.
• Define CI/CD/CT pipelines across ML and GenAI workloads.
• Establish guardrails for monitoring, governance, cost controls, and security.

Skills

AWS

SageMaker

Databricks

MLOps

GenAI

RAG architectures

Security

FinOps

CI/CD

Kubernetes

Tools

SageMaker

Databricks

EKS

IAM

CloudWatch

Terraform

Job description

Job Description

We are seeking a senior MLOps Architect to design and scale a modern ML and Generative AI platform across AWS. This role will own the architecture for traditional ML and LLM/Generative AI pipelines, ensuring production reliability, governance, cost optimization (FinOps), and enterprise-grade security. The ideal candidate has deep expertise in AWS, SageMaker, Databricks, Atlan (data catalog/governance), and modern MLOps tooling, and understands how to operationalize LLMs, RAG systems, and foundation models within a governed, scalable MLOps stack. This is a strategic, hands‑on architecture role responsible for integrating GenAI capabilities into an enterprise ML platform.

What you’ll Do:

MLOps & GenAI Platform Architecture

• 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 o Feature engineering / embedding generation o 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.

Generative AI & LLM Operationalization

• 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.

Deployment, Monitoring & Reliability

• 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

FinOps & Cost Optimization

• Implement cost tracking for:
• • Training workloads o GPU utilization
  • Inference endpoints o 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.

Platform Enablement & Standards

• 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.

What We’re Looking for

• 6+ years of experience in ML engineering, data engineering, or MLOps roles.
• Proven experience architecting ML platforms in AWS.
• Strong hands‑on experience with SageMaker (training, pipelines, deployment).
• Experience operationalizing LLM or Generative AI systems in production.
• Experience building RAG pipelines and integrating vector databases.
• Experience working with Databricks in production.
• Experience implem