Architecture / architect

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 (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).

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:

Vector database integration.

Document ingestion and chunking strategies.

Retrieval evaluation and monitoring.

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).

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). 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). 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 building RAG pipelines and integrating vector databases.

Experience working with Databricks in production.

Experience implementing data governance and catalog systems (e.g., Strong understanding of observability and monitoring for ML systems.

Experience implementing cloud cost optimization strategies (FinOps).

Experience with foundation model fine‑tuning and parameter‑efficient methods.

Familiarity with AI risk management, bias mitigation, and safety controls.

Experience supporting regulated or data‑sensitive environments.

Platform‑level architectural thinking.

Ability to balance scalability, governance, security, performance, and cost.

Hands‑on ability to move from architecture design to implementation.

Health, dental, vision insurance (UnitedHealthcare).

Flexible Spending Account, Lifestyle Spending Account.

Fully paid disability insurance.

Paid maternity and parental leave beyond state‑mandated policies.

Tuition reimbursement up to $5,000 annually.

Travel reimbursement for work‑related travel.

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.

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