AI/ML Architect

Responsibilities

• Architect and deliver end-to-end AI-powered solutions for high-tech clients using GCP-native components and Agentic AI frameworks.
• Design and implement scalable backend services using Python (FastAPI, Flask, or Django) and integrate them with LLMs and autonomous agent frameworks (LangChain, AutoGen, CrewAI).
• Lead the design and implementation of frontend applications using React, Angular, or Vue, ensuring seamless UX/UI integration with AI capabilities.
• Collaborate with clients, product managers, and engineering teams to capture business requirements and convert them into technical roadmaps.
• Drive technical workshops, POCs, and architectural reviews focused on AI/ML and cloud transformation strategies.
• Implement and optimize vector database integrations (e.g., Pinecone, Weaviate, FAISS) and embedding pipelines on GCP.
• Define and enforce best practices in cloud-native DevOps, microservices, and CI/CD automation using GCP tools like Cloud Build, Artifact Registry, and Cloud Monitoring.
• Provide architectural guidance and mentorship to distributed engineering teams following Agile delivery models.

Required Skills

• 10+ years of experience in full stack architecture and software engineering, ideally in high-tech product or platform environments.
• Strong hands-on experience with Python backend frameworks (FastAPI, Flask, Django).
• Hands-on experience with Agentic AI frameworks such as LangChain, AutoGen, or CrewAI.
• Solid experience with GCP services including Vertex AI, BigQuery, Pub/Sub, Cloud Storage, Cloud Functions, and Cloud Run.
• Expertise in REST, GraphQL, microservices architecture, and API gateways.
• Proficiency in Docker, Kubernetes (GKE preferred), and CI/CD pipelines using Cloud Build or equivalent.
• Strong communication skills and ability to engage with both technical and business stakeholders.
• Experience working with Agile methodologies and distributed delivery teams.

Preferred Skills

• Proficient in frontend development using Angular with a solid understanding of UX patterns.
• GCP Certification (e.g., Professional Cloud Architect, Professional Data Engineer) is a strong plus.
• Experience in CI/CD implementation on DevOps platforms (e.g., GitLab CI/CD, Cloud Build, Jenkins, or GitHub Actions).
• Familiarity with multi-cloud deployments (AWS, Azure) in addition to GCP.