Description

You will define the AI/ML reference architecture and solution blueprints across batch/streaming ML, LLM+RAG, and multimodal systems.

Responsibilities

• Lead end-to-end solution design, covering data ingestion through inference and monitoring.
• Architect LLM applications (chatbots, copilots, agents) incorporating RAG, safety, and guardrails.
• Own MLOps/LLMOps processes, including CI/CD for models, registry, lineage, and drift monitoring.
• Drive performance and cost optimization across throughput, latency, and SLA targets.
• Establish security, compliance, and governance standards for AI systems, including PII handling.

Required Skills

• 15+ years of overall engineering experience, with 4+ years specifically in AI/ML solution architecture.
• Bachelor's or Master's in Computer Science, Engineering, Data/AI or related field.
• Proven experience deploying AI systems in production at scale, including LLM or classical ML.
• Strong, hands-on proficiency in Python.
• Experience designing and deploying solutions on cloud-native architectures (AWS, Azure, GCP).
• Expertise in LLM/RAG design: retrieval pipelines, embeddings, prompt orchestration, and evaluation.
• Familiarity with MLOps/LLMOps tooling: CI/CD, model registry, feature stores, and lineage.
• Experience with orchestration frameworks like Airflow or Kubeflow.
• Knowledge of core AI/ML concepts: feature engineering, model selection, and training pipelines.

Key Skills

Education

Bachelor's or Master's degrees