It is a remote position in US. Please note 0 to 25% travek may be needed to visit client in US. And the travel will be based on the need.
Required Qualifications
• Experience: 11 to 16 years in software, data and solution architecture, including at least 3 years designing and delivering production GenAI/LLM solutions.
• Hands-on engineering: Strong Python (plus Java, TypeScript or similar), REST/GraphQL APIs and event-driven integration. Current in code, not only diagrams.
• RAG and vector search: Demonstrated delivery of RAG systems with vector databases (e.g., Pinecone, pgvector, Azure AI Search, Vertex AI Vector Search, OpenSearch, Weaviate or Milvus). Retrieval quality tuning and evaluation.
• LLM platforms: Hands-on with Anthropic, OpenAI and Gemini APIs, including tool/function calling, structured outputs, prompt engineering, context management and cost control.
• Cloud: Production experience on Google Cloud Platform (strongly preferred), Azure or AWS: compute and containers (Kubernetes), managed AI services (Vertex AI, Azure OpenAI/AI Foundry, Bedrock), storage, serverless and CI/CD.
• Data architecture: Data modelling, lakehouse/warehouse patterns, ingestion pipelines, metadata, lineage and data quality. Snowflake, BigQuery or Databricks experience.
• Application architecture: Microservices, API design, integration patterns, resilience and non-functional requirements.
• Security and governance: RBAC, identity (OAuth2/OIDC, Entra ID, IAM), APIM gateways, encryption and compliance (e.g., HIPAA, GDPR, SOC 2).
• Architecture governance: Proven track record presenting to and securing approval from ARBs, with clear ADRs and trade-off analysis.
• Communication: Clear written and verbal communication with executives and engineers. Experience leading without direct authority.
