AI Engineer (Agentic AI, GenAI)

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 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

• 7+ 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.