全职 Solutions Architect 工作, 薪水, Elliott Moss Consulting 公司招聘中 - Ricebowl

We are seeking an experienced Technical Lead / Solution Architect to design and deliver enterprise-grade, multi-cloud and AI-enabled solutions. The role will provide technical leadership across application, data, integration, security, cloud, and observability, with a strong focus on Generative AI, Agentic AI, RAG, and LLMOps.

The successful candidate will lead client engagements, architecture design, proof of concepts, and technical delivery across AWS, Azure, and GCP environments.

Key Responsibilities::

• Translate business requirements into scalable GenAI and Agentic AI solution architectures, including RAG and AI agent-based solutions.
• Own end-to-end architecture across applications, data, integrations, security, infrastructure, and observability.
• Provide technical leadership for client opportunities, solution discussions, and implementation engagements.
• Lead the design and development of POCs and MVPs, guiding engineering teams through build, deployment, and operationalisation.
• Establish and implement LLMOps practices, including model evaluation, tracing, observability, guardrails, prompt management, versioning, and quality metrics.
• Design batch and streaming data architectures, vector search capabilities, APIs, events, and agent/tool integration contracts.
• Evaluate and recommend appropriate cloud platforms, AI models, managed services, and open-source technologies based on cost, performance, scalability, and security.
• Design secure solutions using Zero Trust, IAM, OAuth2/OIDC, secrets management, KMS, data classification, and access controls.
• Define APIs and integration architectures using REST, gRPC, GraphQL, and event-driven patterns.
• Lead architecture reviews, design reviews, code reviews, and technical governance activities.
• Plan technical roadmaps, delivery backlogs, estimates, dependencies, and implementation strategies across multidisciplinary teams.
• Work closely with clients and stakeholders to communicate technical decisions, risks, benefits, cost considerations, and ROI.
• Coach and mentor engineering teams and establish reusable architecture patterns, templates, and reference implementations.
• Manage multiple client opportunities and technical initiatives while maintaining strong stakeholder relationships.

Technical Requirements::

• Strong hands-on experience across AWS, Azure, and GCP.
• Minimum 3 years of hands-on experience in each of AWS, Azure, and GCP environments.
• At least 1 year of hands-on experience with Generative AI and Agentic AI technologies.
• Experience designing and delivering production-grade RAG and AI agent solutions.

Hands-on experience with at least one AI/agent framework, such as:

• LangChain / LangGraph
• DSPy
• OpenAI or Anthropic tool use
• Databricks Agents
• Equivalent AI agent frameworks
• Strong experience with LLMOps, including:
• AI model and application evaluation
• LLM judges and task-based metrics
• MLflow / OpenTelemetry tracing and observability
• Prompt and version management
• CI/CD for AI applications
• AI safety and guardrails
• Strong data platform experience with Delta Lake, Apache Iceberg, or Apache Hudi.
• Experience with streaming technologies such as Kafka, Kinesis, or Google Pub/Sub.
• Hands-on experience with vector databases/search technologies such as Databricks Vector Search, pgvector, Pinecone, Milvus, or Vespa.
• Experience with Kubernetes, containers, serverless architectures, and Infrastructure as Code using Terraform and/or CloudFormation.
• Strong understanding of microservices, distributed systems, API design, and event-driven architecture.
• Good understanding of web and mobile application architectures.

Qualifications & Experience::

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline.
• 12+ years of experience in enterprise application, cloud, data platform, or solution architecture.
• Minimum 2 years of experience designing and delivering production-grade Generative AI solutions.
• Strong exposure to Data Science and Machine Learning.
• Proven experience leading architecture and technical delivery across complex enterprise environments.
• Strong client-facing, stakeholder management, communication, and influencing skills.
• Ability to manage multiple technical opportunities and competing priorities.
• Strong analytical and problem-solving skills with the ability to translate complex technical concepts into clear business outcomes.

Required Certifications::

• Candidates should hold relevant certifications across the following areas:
• AWS, Microsoft Azure, or Google Cloud AI Certifications
• AWS, Microsoft Azure, or Google Cloud Data Science / Machine Learning Certifications
• AWS, Microsoft Azure, or Google Cloud Solution Architect Certifications
• TOGAF 9 Certification
• Other equivalent industry-recognised architecture certifications are an advantage.