This role is for one of our clientsIndustry: Technology, Information and MediaSeniority level: Mid-Senior levelMin Experience: 5 yearsJobType: full-timeWe 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 HaveExperience 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 SkillsPython
- Backend Engineering
- Machine Learning
- Artificial Intelligence
- TensorFlow
- PyTorch
- FastAPI
- Docker
- Kubernetes
- CI/CD
- MLflow
- Cloud Platforms
