Senior AI Engineer

About the Role

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.

Cloud platforms (AWS, Azure, GCP) or on-premises deployment.

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