Machine Learning Operations (MLOps) Architect - Generative AI Focus

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.