Senior AI Engineer

We are seeking a Senior AI Engineer with a proven record of designing, developing, and deploying advanced AI solutions to real-world problems. You will lead the full lifecycle of AI projects, from research and prototyping to production-grade systems, including solutions running on workstations and edge or physical AI platforms.

Key Responsibilities

• Design, train, and optimize AI models across machine learning, deep learning, reinforcement learning, NLP, computer vision, and generative AI.
• Build and fine-tune Transformer-based architectures, diffusion models, and large language models (LLMs).
• Design and implement agentic AI systems and AI workflows, including orchestration, tool use, and multi-step reasoning pipelines.
• Apply transfer learning, fine-tuning, and parameter-efficient techniques to adapt models to specific domains.
• Scale AI proofs of concept (PoCs) into robust, maintainable, product-level solutions.
• Optimize models for efficient inference through quantization, pruning, distillation, and hardware-aware tuning.
• Develop and deploy AI solutions on GPU workstations and edge or embedded platforms, including physical AI applications.
• Define and run benchmarking and testing frameworks to evaluate model accuracy, robustness, latency, throughput, and resource usage.
• Deploy models and integrate them with existing systems, APIs, and enterprise infrastructure.
• Collaborate with cross-functional teams (product, data, software, and domain experts) and mentor junior engineers.
• Stay current with AI research and recommend new techniques and tools.

Required Technical Skills

Core AI Domains

• Machine Learning: supervised and unsupervised learning, feature engineering, model selection, and evaluation.
• Reinforcement Learning: policy optimization, reward design, and applied RL for decision-making or control.
• Deep Learning and Neural Networks: CNNs, RNNs, Transformers, and attention-based architectures.
• Natural Language Processing: language modeling, text classification, retrieval, embeddings, and conversational systems.
• Computer Vision: detection, segmentation, tracking, and multimodal vision-language models.
• Generative AI: LLMs, diffusion models, prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
• Agentic AI and AI Workflows: designing autonomous, tool-using, multi-agent pipelines.

Optimization and Performance

• Model quantization, pruning, and knowledge distillation.
• Transfer learning and efficient fine-tuning (e.g., LoRA, PEFT).
• Benchmarking and performance testing: accuracy metrics, latency, throughput, memory footprint, stress and regression testing, and reproducible evaluation pipelines.

Frameworks and Libraries

• PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, ONNX, OpenCV, and related tools.

Workstation, Edge, and Physical AI

• GPU computing (CUDA), inference runtimes (TensorRT, ONNX Runtime, OpenVINO), and containerized environments.
• Experience with edge and embedded platforms (e.g., NVIDIA Jetson or similar) and real-time constraints.
• Familiarity with sensor integration, robotics, and simulation environments is an advantage.

Deployment and Integration

• Model serving and APIs (REST, gRPC), Docker, Kubernetes, and CI/CD pipelines.
• MLOps practices: experiment tracking, model versioning, monitoring, and retraining.
• Integration of AI components with existing software systems and enterprise architecture.

Appreciated Skills

• Data Science: statistical analysis, exploratory data analysis, hypothesis testing, and data visualization.
• Data Engineering: data pipelines, ETL/ELT, SQL and NoSQL databases, and big data tools such as Spark.
• Experience with data quality, labeling strategies, and synthetic data generation.

Qualifications

• Minimum: Bachelor's degree in Computer Science, Artificial Intelligence, or a related field.
• Preferred: Master's or PhD specialized in AI, machine learning, or data science.
• Proven, documented experience designing, developing, and deploying advanced AI solutions in real-world use cases.
• Demonstrated track record of taking AI projects from PoC to production.
• Strong problem-solving, communication, and technical leadership skills.
• Publications, patents, or open-source contributions are a plus.

What We Offer

• Competitive compensation and benefits
• Opportunity to work on impactful, cutting-edge AI projects
• Professional development and career growth

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