Senior AI Engineer
Role Overview
Senior AI Engineer responsible for designing, developing, and deploying advanced AI solutions for real-world problems.
Lead projects through research, prototyping, and production, including systems on workstations and edge or physical AI platforms.
Key Skills for This Role
Full Job Posting
About the role
Senior AI Engineer responsible for designing, developing, and deploying advanced AI solutions for real-world problems.
Lead projects through research, prototyping, and production, including systems on workstations and edge or physical AI platforms.
Responsibilities
- Design and optimize machine-learning, reinforcement-learning, deep-learning, NLP, computer-vision, and generative AI models.
- Build and fine-tune Transformer architectures, diffusion models, LLMs, agentic systems, and multi-step AI workflows.
- Apply transfer learning and efficient fine-tuning; scale proofs of concept into maintainable production solutions.
- Optimize inference with quantization, pruning, distillation, and hardware-aware techniques; deploy to GPU, edge, or embedded platforms.
- Benchmark accuracy, robustness, latency, throughput, and resource usage; deploy models and integrate APIs with enterprise systems.
- Collaborate across product, data, software, and domain teams, mentor junior engineers, and track AI research.
Technical skills
- Core areas include supervised and unsupervised learning, feature engineering, model evaluation, reinforcement learning, neural networks, NLP, computer vision, generative AI, and agentic workflows.
- Optimization includes quantization, pruning, distillation, transfer learning, LoRA, and PEFT; evaluation includes reproducible benchmarking and performance testing.
- Frameworks include PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, ONNX, and OpenCV.
- GPU, edge, and deployment tools include CUDA, TensorRT, ONNX Runtime, OpenVINO, Docker, Kubernetes, CI/CD, REST, and gRPC.
- MLOps includes experiment tracking, versioning, monitoring, and retraining; deployment may be cloud or on premises.
- Sensor integration, robotics, and simulation are advantageous; data science, data engineering, SQL/NoSQL, and Spark are appreciated skills.
Qualifications
- Minimum bachelor's degree in Computer Science, Artificial Intelligence, or a related field.
- Master's or PhD specializing in AI, machine learning, or data science is preferred.
- Documented real-world AI solution delivery experience and a demonstrated record of taking projects from proof of concept to production.
- Strong problem-solving, communication, and technical leadership skills.
- Publications, patents, or open-source contributions are a plus.
What the employer offers
- Competitive compensation and benefits, impactful AI projects, and professional development and career growth.
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