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 (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.
• 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., 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.
• 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.
• Fin Ops & Cost Optimization
  • Implement cost tracking for:
    • Training workloads (GPU utilization)
    • Inference endpoints (Token consumption for LLM APIs)
    • 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.

• 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).
• 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…