AI Engineer/Architect

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.