Compunnel
We are seeking a highly experienced AI/ML Technical Architect to lead the design and development of advanced agentic AI systems using Google Cloud Platform (GCP).
This role involves building autonomous AI systems capable of reasoning, planning, and executing complex tasks using large language models and orchestration frameworks.
The ideal candidate will have a strong background in cloud-native architecture, AI/ML engineering, and multi-agent systems.
Key Responsibilities
Design and implement agentic AI architectures using Google Vertex AI models (e.g., Gemini, PaLM, Codey).
Develop and deploy AI agents using LangChain and Model Context Protocol (MCP).
Build scalable, production-ready AI systems leveraging GCP services such as Vertex AI Pipelines, Matching Engine, and AI Platform.
Implement multi-agent systems with advanced reasoning, planning, and tool-use capabilities.
Design and optimize vector databases and embedding systems for retrieval-augmented generation (RAG).
Integrate AI agents with APIs, databases, and enterprise systems.
Establish MLOps practices for deployment, monitoring, and continuous improvement of AI agents.
Collaborate with stakeholders to define architecture strategies and ensure alignment with business goals.
Mentor junior engineers and lead technical teams in AI/ML solution delivery.
Required Qualifications
10+ years of experience in software engineering, with 3+ years focused on AI/ML systems. 2+ years of hands-on experience with production LLM applications. Expertise with Google Vertex AI suite (Gemini Pro/Ultra, PaLM 2, Codey, Imagen). Proficiency in Python (5+ years) and frameworks such as FastAPI, Flask, or Django. Experience with LangChain, LangGraph, and agent orchestration frameworks. Strong understanding of agentic AI patterns (ReAct, Chain-of-Thought, Tree of Thoughts). Hands-on experience with GCP services: Vertex AI Workbench, Pipelines, BigQuery, Cloud Storage, Cloud SQL, Pub/Sub. Experience with containerization (Docker, Kubernetes) and Infrastructure as Code (Terraform). Proficiency in version control (Git) and CI/CD pipelines (Cloud Build, GitHub Actions). Strong SQL skills and experience with structured and unstructured data processing. Preferred Qualifications
Experience with agent frameworks such as AutoGen, CrewAI, or Semantic Kernel. Familiarity with graph databases (Neo4j, Amazon Neptune). Exposure to other cloud platforms (AWS Bedrock, Azure OpenAI). Understanding of knowledge graph construction, vector databases (e.g., Pinecone, Weaviate), and embedding systems. Background in distributed systems, microservices architecture, and real-time AI applications. Awareness of AI safety, alignment, and responsible AI practices. Certifications
Google Cloud Professional ML Engineer or other AI/ML-related certifications (preferred).
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10+ years of experience in software engineering, with 3+ years focused on AI/ML systems. 2+ years of hands-on experience with production LLM applications. Expertise with Google Vertex AI suite (Gemini Pro/Ultra, PaLM 2, Codey, Imagen). Proficiency in Python (5+ years) and frameworks such as FastAPI, Flask, or Django. Experience with LangChain, LangGraph, and agent orchestration frameworks. Strong understanding of agentic AI patterns (ReAct, Chain-of-Thought, Tree of Thoughts). Hands-on experience with GCP services: Vertex AI Workbench, Pipelines, BigQuery, Cloud Storage, Cloud SQL, Pub/Sub. Experience with containerization (Docker, Kubernetes) and Infrastructure as Code (Terraform). Proficiency in version control (Git) and CI/CD pipelines (Cloud Build, GitHub Actions). Strong SQL skills and experience with structured and unstructured data processing. Preferred Qualifications
Experience with agent frameworks such as AutoGen, CrewAI, or Semantic Kernel. Familiarity with graph databases (Neo4j, Amazon Neptune). Exposure to other cloud platforms (AWS Bedrock, Azure OpenAI). Understanding of knowledge graph construction, vector databases (e.g., Pinecone, Weaviate), and embedding systems. Background in distributed systems, microservices architecture, and real-time AI applications. Awareness of AI safety, alignment, and responsible AI practices. Certifications
Google Cloud Professional ML Engineer or other AI/ML-related certifications (preferred).
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