We are seeking a Senior AI Systems Engineer who combines the mindset of a backend engineer with a deep understanding of AI/ML workflows. This role is perfect for someone who can bridge the gap between cutting-edge AI research and real-world, large-scale deployment—owning everything from data pipelines to APIs, orchestration, and monitoring.

This is a hands-on engineering role, where you’ll architect and implement scalable AI systems that are robust, reproducible, and production-ready.

What You’ll Do

Architect Scalable AI Systems: Design and implement production-grade architectures with a strong emphasis on backend services, orchestration, and automation.

Build End-to-End Pipelines: Develop modular pipelines for data ingestion, preprocessing, training, serving, and continuous monitoring.

Develop APIs & Services: Build APIs, microservices, and backend logic to seamlessly integrate AI models into real-time applications.

Operationalize AI: Collaborate with DevOps and infrastructure teams to deploy models across cloud, hybrid, and edge environments.

Enable Reliability & Observability: Implement CI/CD, containerization, and monitoring tools to ensure robust and reproducible deployments.

Optimize Performance: Apply profiling, parallelization, and hardware-aware optimizations for efficient training and inference.

Mentor & Guide: Support junior engineers by sharing best practices in AI engineering and backend system design.

What You’ll Bring

Programming Expertise: Strong backend development experience in Python (bonus: Go, Rust, or Node.js).

Frameworks & APIs: Hands-on with FastAPI, Flask, or gRPC for building high-performance services.

AI Lifecycle Knowledge: Deep understanding of model development workflows—data processing → training → deployment → monitoring.

Systems & Infrastructure: Strong grasp of distributed systems, Kubernetes, Docker, CI/CD pipelines, and real-time data processing.

MLOps Tools: Experience with MLflow, DVC, Weights & Biases, or similar platforms for experiment tracking and reproducibility.

Cloud & Containers: Comfort with Linux, containerized deployments, and major cloud providers (AWS, GCP, or Azure).

Nice to Have

Experience with computer vision models (YOLO, UNet, transformers).

Exposure to streaming inference systems (Kafka, NVIDIA DeepStream).

Hands-on with edge AI hardware (NVIDIA Jetson, Coral) and optimizations (TensorRT, ONNX).

Experience in synthetic data generation or augmentation.

Open-source contributions or research publications in AI/ML systems.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field.

5+ years of software engineering experience, ideally in AI/ML-driven products.

Demonstrated success in designing, building, and scaling production-ready AI systems.

Key Skills

Python · Backend Engineering · Machine Learning · Artificial Intelligence · TensorFlow · PyTorch · FastAPI · Docker · Kubernetes · CI/CD · MLflow · Cloud Platforms