# Machine Learning Operations (MLOps) Architect - Generative Al Focus

**Company:** [Kapitus](https://hotfix.jobs/companies/kapitus)
**Location:** Arlington, VA
**Role:** ML Engineering
**Salary:** $118k – $189k/yr
**Experience:** 6+ years
**Skills:** AWS, SageMaker, Databricks, Atlan, Kubernetes, EKS, Docker, Terraform, CloudFormation, Python, MLflow, RAG, LLMs, Vector Databases, CI/CD
**Posted:** 2026-05-05

> Designs and scales enterprise MLOps platform focused on Generative AI and LLMs on AWS, owning architecture for pipelines, deployment, monitoring, governance, and cost optimization. Requires 6+ years ML engineering experience with SageMaker, RAG, and production LLM operationalization.

## Job Description

## 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, 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.

### 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 (GPU utilization), inference endpoints (token consumption), 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 implementing data governance and catalog systems (e.g., Atlan).
- Strong understanding of CI/CD principles for ML and GenAI.
- Experience with containerization (Docker) and orchestration (Kubernetes/EKS).
- Deep knowledge of infrastructure-as-code (Terraform, CloudFormation).
- Strong understanding of observability and monitoring for ML systems.
- Experience implementing cloud cost optimization strategies (FinOps).
- Strong Python proficiency.
- Experience with foundation model fine-tuning and parameter-efficient methods.
- Experience implementing model registries and experiment tracking tools.
- Experience designing feature stores and embedding stores.
- Familiarity with AI risk management, bias mitigation, and safety controls.
- Experience supporting regulated or data-sensitive environments.
- Platform-level architectural thinking.
- Deep understanding of how to integrate GenAI into enterprise ML ecosystems.
- Ability to balance scalability, governance, security, performance, and cost.
- Strong technical leadership and cross-functional collaboration skills.
- Hands-on ability to move from architecture design to implementation.

**Compensation:** Competitive Base Salary Range of $117,800 – $189,000. Annual Incentive Compensation Eligibility – Up to 10% annually.

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