AI Systems Architect
- Micasa Global
3 Days Ago
NA
C2C
San Francisco-CA, Charlotte NC-NC, Dallas-TX
9-12 Years
Required Skills: AI, ML, Agent-based architectures, frameworks, Prompt engineering, Model context protocol servers, LLM, AWS, Azure, GCP, Python, JAVA, Go, Vector databases, Hybrid search, Data Freshness Strategies, Memory Systems, Graph Reasoning, BM25, RAG frameworks, Orchestration frameworks, Data pipelines, streaming architectures
Job Description
We are seeking an experienced AI Systems Architect to design, build, and scale high-performance distributed AI systems. The ideal candidate will have deep expertise in GenAI, LLMs, and cloud-native architectures, along with hands-on experience in building enterprise-scale AI/ML platforms and agent-based systems.
Must-Have Skills
Strong experience in designing and implementing high-performance, large-scale distributed systems
Proven experience in implementing and deploying AI/ML platforms at scale
Expertise in building agent-based architectures, evaluation frameworks, and prompt/context engineering
Knowledge of MCP (Model Context Protocol) servers
Hands-on experience in LLM inference optimization, including batching and caching strategies
Strong experience with Kubernetes and cloud infrastructure (AWS/Azure/GCP)
Proficiency in at least one programming language (Python, Java, Go, etc.)
Expertise in designing agent data stacks & retrieval systems, including:
Vector databases
Hybrid search
Data freshness strategies
Memory systems
Graph reasoning
BM25 and advanced retrieval techniques
Strong experience in designing and implementing high-performance, large-scale distributed systems
Proven experience in implementing and deploying AI/ML platforms at scale
Expertise in building agent-based architectures, evaluation frameworks, and prompt/context engineering
Knowledge of MCP (Model Context Protocol) servers
Hands-on experience in LLM inference optimization, including batching and caching strategies
Strong experience with Kubernetes and cloud infrastructure (AWS/Azure/GCP)
Proficiency in at least one programming language (Python, Java, Go, etc.)
Expertise in designing agent data stacks & retrieval systems, including:
Vector databases
Hybrid search
Data freshness strategies
Memory systems
Graph reasoning
BM25 and advanced retrieval techniques
Key Responsibilities
Architect and deliver scalable, high-performance distributed systems
Design and deploy AI/ML and GenAI platforms at enterprise scale
Build and manage agent-based architectures, including:
Prompt and context engineering
MCP servers
Evaluation frameworks
Optimize LLM inference pipelines for latency, throughput, and efficiency
Design and implement agent data & retrieval systems (vector DBs, hybrid search, memory, graph-based reasoning)
Lead Kubernetes-based, cloud-native deployments
Provide technical leadership, architecture governance, and hands-on mentoring to engineering teams
Architect and deliver scalable, high-performance distributed systems
Design and deploy AI/ML and GenAI platforms at enterprise scale
Build and manage agent-based architectures, including:
Prompt and context engineering
MCP servers
Evaluation frameworks
Optimize LLM inference pipelines for latency, throughput, and efficiency
Design and implement agent data & retrieval systems (vector DBs, hybrid search, memory, graph-based reasoning)
Lead Kubernetes-based, cloud-native deployments
Provide technical leadership, architecture governance, and hands-on mentoring to engineering teams
Nice to Have
Experience with RAG (Retrieval-Augmented Generation) frameworks
Familiarity with multi-agent systems and orchestration frameworks
Exposure to real-time data pipelines and streaming architectures
Experience with RAG (Retrieval-Augmented Generation) frameworks
Familiarity with multi-agent systems and orchestration frameworks
Exposure to real-time data pipelines and streaming architectures
