Technical Architect - ML - GenAI
New
Q
QuantiphiGenerative AI
Remote (US)Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ Years
- Required Skills
- AWSPythonLLMGenerative AILangChain
Requirements
- 8+ years of relevant hands-on technical experience implementing and developing cloud ML solutions on AWS.
- Proven experience using AWS Sagemaker and Bedrock leveraging different types of data sources, training jobs, real-time and batch applications.
- Design and implement agentic AI architectures using frameworks such as LangChain, Strand Agents, etc.
- Hands-on experience with Amazon AgentCore for building, deploying, and scaling production-grade agentic AI applications.
- Architect and deploy scalable AI solutions on AWS, leveraging services like Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.
- Proficiency in working with LLM APIs, including API integration and multi-model orchestration strategies.
- Hands-on experience fine-tuning or optimizing large language models (LLM).
- Strong expertise in Vector Databases, including indexing strategies, embedding generation, and similarity search.
- Develop and maintain Model Context Protocol (MCP) implementations.
- Experience with at least one workflow orchestration tool like Airflow, StepFunctions, SageMaker Pipelines, or Kubeflow.
- Solid understanding of Deep Learning concepts including Transformers, BERT, Attention models, tokenization, and embeddings.
Responsibilities
- Design and implement GenAI solutions using AWS Bedrock and Agentcore.
- Define architecture for LLM-based applications, including RAG pipelines and agentic workflows.
- Develop and orchestrate agentic AI workflows, enabling multi-step reasoning, tool usage, and task automation.
- Build and manage RAG pipelines, including embeddings, retrieval mechanisms, and vector databases.
- Integrate LLM capabilities into enterprise applications via APIs and backend services.
- Design and optimize prompt engineering strategies for accuracy, relevance, and performance.
- Ensure model evaluation, monitoring, and optimization for latency, cost, and response quality.
- Collaborate with application, data, and platform teams for end-to-end solution delivery.
- Define best practices for security, governance, and responsible AI usage.
- Provide technical leadership and mentor team members while remaining hands-on.
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