Back End Developer

About the Role

We’re looking for a Backend Developer who’s passionate about building the infrastructure behind real AI systems - not just integrating tools, but working directly with models, data pipelines, and intelligent systems. This role is ideal for engineers who want to go beyond CRUD applications and contribute to the architecture, scaling, and deployment of AI technologies - like large language models, custom ML systems, vector search, and real-time inference engines.

What You’ll Do

• Collaborate with AI/ML teams to build backend infrastructure for model training, serving, and monitoring.
• Design and implement inference APIs, model versioning systems, and custom ML workflows.
• Build and maintain scalable, high-performance systems that support AI-native features such as recommendation engines, RAG pipelines, chat systems, and more.
• Handle data orchestration: preprocessing, feature engineering pipelines, and real-time data flows.
• Work on batch and real-time processing systems to support AI use cases.
• Develop backend components that interact with vector databases, embedding models, and semantic search logic.
• Own the productionization of AI models, including deployment, performance tuning, and observability.

What We’re Looking For

• Strong proficiency in backend development (Python, Java, or Go preferred).
• Experience integrating machine learning models into production applications.
• Understanding of ML concepts like model serving, training pipelines, embeddings, and transformers.
• Familiarity with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
• Comfortable working with asynchronous systems, message queues (Kafka, RabbitMQ), and REST/gRPC APIs.
• Experience with data systems: SQL/NoSQL databases, time-series DBs, caching systems.
• Knowledge of CI/CD, logging, observability, and production monitoring practices.

Bonus Skills

• Experience working with LLMs, RAG architectures, or vector databases (like Pinecone, Weaviate, FAISS).
• Exposure to tools like Ray, MLflow, Airflow, DVC, or other ML Ops tooling.
• Experience with GPU-based workloads, inference optimization, or quantization.
• Familiarity with frameworks like FastAPI, Flask, Spring Boot.