Machine Learning Operations (MLOps) Architect - Generative Al Focus

Kapitus•Arlington, VA

8d•Remote

About The Position

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.

Requirements

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.

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.

Benefits

Competitive Base Salary Range of $117,800 – $189,000
Annual Incentive Compensation Eligibility – Up to 10% annually
Health Insurance: Comprehensive medical, dental, and employer-paid vision plans through UnitedHealthcare (UHC), with various coverage levels available to meet the needs of our employees and their families. Additional perks through UHC include: Sweat Equity, free subscription to the Calm App, UHC rewards, Real Appeal, and Quit For Life.
Flexible Spending Account: Set aside pre-tax dollars from your paycheck to pay for qualified out-of-pocket medical, dental, vision, pharmacy or dependent care expenses.
Lifestyle Spending Account: Employer sponsored post-tax benefits that allow reimbursement for expenses related to physical, mental and financial well-being.
100% Company Paid Insurances: Kapitus fully covers the cost of basic short-term and long-term disability insurance, as well as vision insurance, ensuring our employees have comprehensive protection without any personal expense.
Voluntary Insurance: Supplemental life insurance as well as enhanced short- and long-term disability coverage are available through Mutual of Omaha, providing additional security for our employees. Additionally, Colonial Accident and Hospitalization insurances are also available, offering further protection against unforeseen events.
Paid Maternity and Parental Leave: Beyond state-mandated leave policies, Kapitus provides company-paid maternity and parental leave, supporting our employees during important family milestones.
Commuter Benefits: We offer pre-tax benefits on parking and commuter expenses to cover travel to and from work.
LifeBalance Program: Enhance your lifestyle with our LifeBalance membership, which offers discounts on outdoor activities, the arts, health, and fitness. Additional benefits include: Pet and car insurance discounts. Financial services such as LegalShield. Relaxation and stress management tools.
Plum Benefits Discount Program: Access exclusive discounts on shows, travel, car rentals, and more, enriching your personal and family life.
Tuition Reimbursement: Pursue further education with up to $5,000 annually in tuition reimbursement, plus opportunities to attend relevant conferences and career development events. Managed through our LSA plan, Kapitus Academy.
Travel Reimbursement: We also offer travel reimbursement for all work-related travel, supporting your involvement in career and personal development activities.
Paid Time Off and Sick Time.
Retirement Benefits: Our 401K plan is managed through Fidelity. To support your long-term financial goals, the company provides a 25% match on your contributions, up to 6% of your annual salary.