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.