Kapitus logo
Kapitus

We believe business owners should be able to focus on running their business, while we take care of the financing.

Machine Learning Operations (MLOps) Architect - Generative AI Focus

OperationsOperationsFull TimeRemoteMid LevelTeam 201-500Since 2006H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

22 days ago

Salary

$117.8K - $189K / year

Seniority

Mid Level

No structured requirement data.

Job Description

Machine Learning Operations (MLOps) Architect - Generative AI Focus

Kapitus

Role 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 - 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 - Inference endpoints - 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. 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). - 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. 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). - Flexible Spending Account: Set aside pre-tax dollars for qualified out-of-pocket expenses. - Lifestyle Spending Account: Employer-sponsored post-tax benefits for well-being expenses. - 100% Company Paid Insurances: Basic short-term and long-term disability insurance, as well as vision insurance. - Voluntary Insurance: Supplemental life insurance and enhanced short- and long-term disability coverage available. - Paid Maternity and Parental Leave: Company-paid leave beyond state-mandated policies. - Commuter Benefits: Pre-tax benefits on parking and commuter expenses. - LifeBalance Program: Discounts on outdoor activities, arts, health, and fitness. - Plum Benefits Discount Program: Exclusive discounts on shows, travel, car rentals, and more. - Tuition Reimbursement: Up to $5,000 annually for further education and career development. - Travel Reimbursement: Support for work-related travel expenses. - Paid Time Off and Sick Time. - Retirement Benefits: 401K plan with a 25% match on contributions, up to 6% of annual salary.

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Flywire logo

Team Lead, Payment Experience, Americas

Flywire

Delivering the most important & complex payments.

Operations22 days ago
Full TimeRemoteTeam 1,001-5,000Since 2011H1B Sponsor

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Canada
Job Closed
Flywire logo

Team Lead, Payment Experience, Americas

Flywire

Delivering the most important & complex payments.

Operations22 days ago
Full TimeRemoteTeam 1,001-5,000Since 2011H1B Sponsor

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The range displayed on this job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and several other factors, including job-related skills, experience, relevant education and training. #LI-Remote

Massachusetts
$55K - $65K / year
Job Closed
Remote VA logo

Operations Assistant

Remote VA

Note: Due to the high volume of applications we receive, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.

Operations22 days ago

Role Description We are looking for a reliable and detail-oriented Operations Assistant to support daily administrative and operational tasks. The ideal candidate is organized, proactive, and comfortable using various communication and task management tools. Work Schedule (UK Time): - Friday – Saturday: 16:00 to 05:00 - Saturday – Sunday: 16:00 to 05:00 - Sunday – Monday: 17:00 to 08:00 Key Responsibilities - Manage and organize daily administrative tasks and workflows - Coordinate with team members and clients via email and messaging platforms - Monitor and update tasks using Trello - Handle email correspondence through Outlook - Communicate updates and follow-ups via WhatsApp - Create and manage invoices using InvoiceSimple - Maintain accurate records and documentation - Assist in general operational support as needed Qualifications - Previous experience in an administrative or operations role - Strong organizational and multitasking skills - Excellent written and verbal communication - Proficiency in: - Outlook - Trello - WhatsApp - InvoiceSimple - High attention to detail and ability to meet deadlines - Ability to work independently with minimal supervision Benefits - Weekly Pay - Work from home - Negotiable rate, depending on the experience

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