AI / ML Architect (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 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.