AI Platform Architect

Our Company
Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. 
We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

Job Description

The candidate should have proven expertise in building scalable platforms that are customer facing and have expertise in evangelizing the platform with customers and with internal stakeholders Expert level knowledge of Cloud Computing including aspects of VPC Network Design, Shared Responsibility Matrix, Cloud databases, No SQL Databases, Data Pipelines on the cloud, VM and VM orchestration, Serverless frameworks. This should be across all 3 major cloud providers (AWS, Azure, GCP), preferably at least in 2 of the 3 Public Clouds Expert level knowledge in Data Ingestion paradigms & use of different types of databases like OLTP, OLAP for specific purposes Hands-on experience with Apache Spark, Apache Flink, Kafka, Kinesis, Pub/Sub, Databricks, Apache Airflow, Apache Iceberg, and Presto. Expertise in designing ML Pipelines for experiment management, model management, feature management, model retraining, A/B testing of models and design of APIs for model inferencing at scale. Proven expertise with Kube Flow, SageMaker/Vertex AI/Azure AI. SME in LLM Serving paradigms, deep knowledge of GPU architectures, distributed training and serving of large language models. Expertise in Model and Data parallel training, expertise with frameworks like DeepSpeed and service frameworks like vLLM etc. Proven expertise in Model finetuning and model optimization techniques to achieve better latencies, better accuracies in results. Be an expert in reducing training and resource requirements of finetuning of LLM and LVM models. Have a wide knowledge of different LLM models and have an opinion on aspects of applicability of each model based the usecases. Proven expertise of having worked on specific customer usecases and having seen delivery of a solution end to end from engineering to production. Proven expertise in DevOps and LLMOps, knowledge of Kubernetes, Docker and container orchestration, and deep knowledge of LLM Orchestration frameworks like Flowise, Langflow, Langgraph.

Skill Matrix

LLM: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama

LLM Ops: ML Flow, Langchain, Langraph, LangFlow, Flowise, LLamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI

Dev Ops: Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus

Databases/Datawarehouse: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL, Aurora, Spanner, Google BigQuery.

Cloud Expertise: AWS/Azure/GCP

Cloud Certifications: AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert

Proficient in Python, SQL, Javascript

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