Senior Software Architect

Requirements:
10+ years of backend development experience, with at least 3+ years in an architectural or senior technical leadership role
Strong hands-on experience with Python in production environments
Proven experience designing and building distributed systems and microservices architectures
Extensive experience with AWS (S3, SNS, Lambda, Batch, DynamoDB, RDS, etc.)
Experience with containerization and orchestration technologies (Docker, Kubernetes, Helm, Terraform, ArgoCD)
Deep understanding of scalability, high availability, resilience, and system design principles
Experience with monitoring and observability tools such as Datadog
Proven track record of delivering large-scale, high-quality SaaS products
Strong understanding of LLM application architecture: prompt/version management, tool/function calling, structured outputs, retrieval-augmented generation (RAG), and agent/workflow patterns.
Proven ability to design for AI reliability and safety: guardrails, policy enforcement, content filtering, hallucination mitigation, and failure-mode design (fallbacks, safe defaults).
Experience with data governance and privacy for AI: PII handling, retention policies, redaction, tenant isolation, and secure logging/tracing in SaaS environments.
Ability to control cost/performance tradeoffs: model selection strategies, caching, batching, streaming, rate limits/quotas, and capacity planning for AI workloads.
Experience integrating AI services into distributed systems on AWS (e.g., scalable inference/RAG pipelines, async jobs, event-driven orchestration).
Advantage

Deep understanding of networking concepts
Experience with event-driven architectures
Experience working with enterprise customers and complex technical environments
Experience with vector databases / embedding pipelines and retrieval systems (indexing, chunking, relevance tuning).
Experience with model gateways / multi-provider routing, prompt registries, and feature flagging for model & prompt rollouts.
Familiarity with security & compliance considerations for AI (SOC2-ready controls, audit trails for AI actions, customer data boundaries).
Experience enabling AI-assisted SDLC / SDD practices (AI in design docs, code review, incident analysis) with governance and standards.