
AI ML Technical Architect
4 days ago
Role Title: AI Platform Engineer
Location: Bangalore (In Person in office when required)
Part of the GenAI COE Team
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
Job Type: Full-time
Experience:
- AIML: 10 years (Required)
- Technical Architect: 10 years (Required)
Work Location: In person
Speak with the employer
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