Senior Dev/ML Engineer for AI SaaS

I'm seeking a senior-level engineering professional to build a multi-tenant, AI-driven SaaS platform focused on real-time data scanning. The platform will also incorporate policy enforcement, compliance, and secure data delivery at scale. Key Requirements: - Real-time data scanning capabilities - Policy enforcement and compliance integration - Secure data delivery mechanisms - Multi-tenant architecture Ideal Skills and Experience: - Expertise in AI and ML - Strong background in DevOps - Proficient in software development - Experience with multi-tenant SaaS platforms - Knowledge of data security and compliance regulations Looking for someone who can ensure robust performance, scalability, and security. More Skillset Snapshot below Core Platform Required Skills • Backend Development: Python or Go (4+ years). • API Gateways: NGINX, Envoy, Kong, or cloud-native gateways. • Policy Engines: Experience with OPA/Cedar or policy-as-code frameworks. • Databases: Postgres (RLS), Redis; vector databases (pgvector, Pinecone) a plus. • Cloud & Containers: Docker, Kubernetes, Helm charts. • Security & Compliance: Encryption (KMS), TLS, GDPR/FERPA familiarity. Nice-to-Haves • Experience with event-driven architecture (Kafka, Pub/Sub, EventBridge). • Knowledge of SaaS billing (Stripe) and RBAC implementation. • Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry). DevOps skills • Containers & Orchestration: Kubernetes, Helm, Docker. • Infrastructure as Code: Terraform or equivalent. • Observability: OpenTelemetry, Prometheus, Grafana, ELK. • Security: KMS-based encryption, TLS cert management, WORM storage. • Cloud Platforms: AWS/GCP/Azure (multi-cloud experience preferred). Nice-to-Haves • Familiarity with API Gateway performance tuning. • Experience in SaaS multi-tenant deployments (RLS or schema-per-tenant). • Background in FinOps/billing event pipelines. AI /ML , Data DLP Skills • ML Frameworks: PyTorch, HuggingFace Transformers, TorchServe. • NLP/DLP Techniques: Text classification, NER, embeddings, vector search. • OCR & Parsing: Tesseract, AWS Textract, Google Doc AI, or Azure Form Recognizer. • Data Engineering: Pipelines with Kafka, Pub/Sub, or EventBridge. • Deployment: ML model serving in production (GPU acceleration optional). Nice-to-Haves • Experience with SaaS AI platforms (SageMaker, Vertex AI). • Knowledge of compliance-focused ML (privacy-preserving AI, data minimization). • Background in vector DBs (pgvector, Pinecone, Weaviate).

Project ID: 39733393