MLOps Architect - Gen Al

Overview

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, and enterprise‑grade security.

• Design and implement scalable ML and LLM infrastructure on AWS (Sage Maker, EKS, S3, IAM, Lambda, Step Functions, Cloud Watch).
• 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:
  • 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., Sage Maker 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, and RAG systems.
• Define latency and token usage metrics, SLAs/SLOs, and 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), and 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.
• Provide experiment tracking; give 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.

• 6+ years of experience in ML engineering, data engineering, or MLOps roles.
• Proven experience architecting ML platforms in AWS.
• Strong hands‑on experience with Sage Maker (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, Cloud Formation).
• Strong understanding of observability and monitoring for ML systems.
• Experience implementing cloud cost optimization strategies (Fin Ops).
• 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.

• Competitive Base Salary Range: $117,800 – $189,000.
• Annual Incentive Compensation: up to 10% of base.
• Health, dental, vision insurance (United Healthcare).
• Flexible Spending Account, Lifestyle Spending Account.
• Fully paid disability insurance.
• Paid maternity and parental leave beyond state‑mandated policies.
• Commuter benefits.
• Life Balance membership discounts.
• Plum Benefits discount program.
• Tuition reimbursement up to $5,000 annually.
• Travel reimbursement for work‑related travel.
• Paid time off and sick time.
• 401(k) plan with 25% match up to 6% of salary.

As set forth in Kapitus’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.