AI Platform Engineer

Role Title: AI Platform Engineer

Location: Bangalore (In Person in office when required)

Key Responsibilities

• Platform Development and Evangelism:

• Build scalable AI platforms that are customer-facing.
• Evangelize the platform with customers and internal stakeholders. • Ensure platform scalability, reliability, and performance to meet business needs.

• Machine Learning Pipeline Design:

• Design ML pipelines for experiment management, model management, feature management, and model retraining.
• Implement A/B testing of models.
• Design APIs for model inferencing at scale.
• Proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI.

  LLM Serving and GPU Architecture:

• Serve as an SME in LLM serving paradigms.
• Possess deep knowledge of GPU architectures.
• Expertise in distributed training and serving of large language models. • Proficient in model and data parallel training using frameworks like DeepSpeed and service frameworks like vLLM.

Model Fine-Tuning and Optimization:

• Demonstrate proven expertise in model fine-tuning and optimization techniques.
• Achieve better latencies and accuracies in model results.
• Reduce training and resource requirements for fine-tuning LLM and LVM models.

  LLM Models and Use Cases:

• Have extensive knowledge of different LLM models.
• Provide insights on the applicability of each model based on use cases. • Proven experience in delivering end-to-end solutions from engineering to production for specific customer use cases.

DevOps and LLMOps Proficiency:

• Proven expertise in DevOps and LLMOps practices.
• Knowledgeable in Kubernetes, Docker, and container orchestration. • Deep understanding of LLM orchestration frameworks like Flowise, Langflow, and 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

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

Cloud Knowledge: AWS/Azure/GCP

Dev Ops (Knowledge): Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus

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

Proficient in Python, SQL, Javascript