AI/ML Architect (Agentic AI, GenAI)

Job Description

About the Company Transnational AI Private Limited is a deep-tech organization building intelligent digital platforms that combine modern event-driven architecture, cloud-native systems, and AI/ML-powered intelligence. Job Role We are hiring a senior Technical Architect to design and lead the backend development and system design for real-time, event-driven microservices that seamlessly integrate AI/ML capabilities. You will work with cutting-edge frameworks such as FastAPI, Kafka, AWS Lambda, and collaborate with data scientists to embed ML models into production-grade systems. 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. Qualification & Skills 10+ years of backend software development experience with 4+ 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.