Key Responsibilities System Architecture & Event-Driven Design • Design and implement event-driven architectures using Apache Kafka to orchestrate distributed microservices and streaming pipelines. • Define scalable message schemas (e.g., JSON/Avro), data contracts, and versioning strategies to support AI-powered services. • Architect hybrid event + request-response systems to balance real-time streaming and synchronous business logic. Backend & AI/ML Integration • Develop Python-based microservices using FastAPI, enabling both standard business logic and AI/ML model inference endpoints. • Collaborate with AI/ML teams to operationalize ML models (e.g., classification, recommendation, anomaly detection) via REST APIs, batch processors, or event consumers. • Integrate model-serving platforms such as SageMaker, MLflow, or custom Flask/ONNX-based services. Cloud-Native & Serverless Deployment (AWS) • Design and deploy cloud-native applications using AWS Lambda, API Gateway, S3, CloudWatch, and optionally SageMaker or Fargate. • Build AI/ML-aware pipelines that automate retraining, inference triggers, or model selection based on data events. • Implement autoscaling, monitoring, and alerting for high-throughput AI services in production. Data Engineering & Database Integration • Ingest and manage high-volume structured and unstructured data across MySQL, PostgreSQL, and MongoDB. • Enable AI/ML feedback loops by capturing usage signals, predictions, and outcomes via event streaming. • Support data versioning, feature store integration, and caching strategies for efficient ML model input handling. Testing, Monitoring & Documentation • Write unit, integration, and end-to-end tests for both standard services and AI/ML pipelines. • Implement tracing and observability for AI/ML inference latency, success/failure rates, and data drift. • Document ML integration patterns, input/output schema, service contracts, and fallback logic for AI systems. Preferred Qualifications • 6+ years of backend software development experience with 2+ years in AI/ML integration or MLOps. • Strong experience in productionizing ML models for classification, regression, or NLP use cases. • Experience with streaming data pipelines and real-time decision systems. • AWS Certifications (Developer Associate, Machine Learning Specialty) are a plus. • Exposure to data versioning tools (e.g., DVC), feature stores, or vector databases is advantageous.
