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Job Description & How to Apply Below
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 AI Engineer 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 Sage Maker, MLflow, or custom Flask/ONNX-based services.
Cloud-Native & Serverless Deployment (AWS)
Design and deploy cloud-native applications using AWS Lambda, API Gateway, S3, Cloud Watch, and optionally Sage Maker 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, Postgre
SQL, and Mongo
DB.
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
7+ years of backend software development experience with 4+ years in AI/ML integration or MLOps.
Strong experience in product ionizing 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.
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