Senior AI Engineer

About the Role

We are in search of a Senior AI Engineer experienced in the full spectrum of AI project development, from conceptual research to delivering robust production systems. The role involves working with a variety of AI solutions spanning machine learning, deep learning, NLP, computer vision, reinforcement learning, generative AI, and deployment on both workstations and edge or physical AI platforms.

Key Responsibilities

• Create, train, and refine AI models across various domains such as machine learning, deep learning, reinforcement learning, natural language processing, computer vision, and generative AI.
• Design and optimize Transformer-based architectures, diffusion models, and large language models (LLMs).
• Develop and implement autonomous AI systems involving tool usage and complex reasoning flows.
• Utilize transfer learning, fine-tuning, and parameter-efficient methods to adapt models to specific application needs.
• Transition AI proofs of concept into scalable, maintainable, and production-ready solutions.
• Enhance model performance for inference efficiency using techniques such as quantization, pruning, and knowledge distillation, considering hardware constraints.
• Deploy AI solutions on GPU workstations and embedded or edge devices, including applications on physical AI systems.
• Establish benchmarking standards and testing protocols to measure model performance in terms of accuracy, robustness, latency, throughput, and resource consumption.
• Integrate AI models with existing organizational systems through APIs and enterprise infrastructure.
• Collaborate with multidisciplinary teams including product managers, software engineers, data scientists, and domain specialists, while mentoring less experienced colleagues.
• Continuously update knowledge of AI research trends and evaluate novel methods and tools for potential adoption.

Technical Expertise Required

• Machine learning techniques encompassing supervised and unsupervised approaches, feature engineering, and model validation.
• Reinforcement learning with focus on policy optimization and reward engineering for decision-making systems.
• Deep learning frameworks and architectures such as CNNs, RNNs, Transformers, and attention mechanisms.
• Natural language processing including language modeling, text classification, embedding technologies, and chatbots.
• Computer vision tasks including detection, segmentation, tracking, and integration of multimodal vision-language frameworks.
• Generative AI methods, including work with large language models (LLMs), diffusion processes, prompt design, fine-tuning, and retrieval-augmented generation (RAG).
• Design and management of agentic AI and multi-agent autonomous pipelines.

Optimization and Performance Improvement

• Model compression techniques like quantization, pruning, and knowledge distillation to optimize inference speed and resource needs.
• Application of transfer learning and parameter-efficient fine-tuning strategies including LoRA and PEFT.
• Development and execution of benchmarking procedures covering accuracy, latency, throughput, memory usage, stress testing, and reproducible evaluation.

Tools, Platforms, and Deployment

• Proficiency in AI frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, ONNX, and OpenCV.
• GPU programming including CUDA and using inference runtimes like TensorRT, ONNX Runtime, and OpenVINO, with deployment in containerized systems.
• Experience deploying AI on embedded/edge devices, for example NVIDIA Jetson, with real-time performance considerations.
• Knowledge of sensor integration, robotics, and simulation is advantageous.
• Expertise with model serving via REST, gRPC APIs, container orchestration using Docker and Kubernetes, and continuous integration/continuous deployment (CI/CD) pipelines.
• Familiarity with MLOps workflows involving experiment tracking, model versioning, monitoring, and automated retraining.
• Integration of AI models within existing enterprise software architectures and deployment on cloud platforms like AWS, Azure, or GCP as well as on-premises.

Additional Appreciated Skills

• Data science capabilities such as statistical analysis, exploratory data examination, hypothesis testing, and visualization.
• Data engineering experience including pipeline creation, ETL/ELT processes, SQL/NoSQL databases, and big data technologies like Spark.
• Expertise in data quality assurance, labeling methodologies, and generating synthetic datasets.

Qualifications

• At least a Bachelor's degree in Computer Science, Artificial Intelligence, or related discipline.
• Advanced degrees (Master's or PhD) with specialization in AI, machine learning, or data science are preferred.
• Established record of successfully designing, implementing, and delivering sophisticated AI applications in real scenarios.
• Proven ability to advance projects from proof of concept to fully operational solutions.
• Strong analytical, communication, and leadership competencies.
• Contributions to publications, patents, or open-source projects are advantageous.

What We Offer

• Competitive salary and benefits package.
• Chance to engage in impactful and innovative AI research and development.
• Support for professional learning and career advancement.