Machine Learning Operations (MLOps) Architect - Generative Al Focus

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 (GPU utilization), inference endpoints (token consumption), 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.

What We’re Looking for

• 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.

Compensation: Competitive Base Salary Range of $117,800 – $189,000. Annual Incentive Compensation Eligibility – Up to 10% annually.