Artificial Intelligence Engineer

We offer flexibility in location for this role, enabling you to support our international customer base and contribute to our continued growth.At Datafusion.AI, we are revolutionizing voice and data capturing and processing for telecommunications and governmental agencies through innovative IP and Voice data stream processing and analytics. Our cutting-edge solutions shape the future of global communication, processing billions of data points daily and directly impacting government operations.We are seeking a talented and exceptional Artificial Intelligence/Machine Learning Engineer who wants to make a difference in the world, has a strong drive for continuous learning and development, is open and collaborative, and never stops striving to improve themselves and the products or services they are responsible for. You will be responsible for developing and optimizing advanced machine learning (ML) models across various domains, including natural language processing (NLP), computer vision, and speech processing. This role requires broad expertise in large language modelsKey ResponsibilitiesDesign, implement, and fine-tune Large Language Models (LLMs) for various NLP tasks, including text classification, entity extraction, sentiment analysis, and conversational AI applications.Develop and optimize diffusion models for generating synthetic data, augmenting data, and creating multimodal content to enhance training datasets.Build and maintain end-to-end machine learning pipelines for text processing, speech recognition, and multimodal data analysis.Implement state-of-the-art transformer architectures and attention mechanisms for various sequence modeling tasks across different data modalities.Research and integrate cutting-edge ML techniques, including few-shot learning, transfer learning, and multi-task learning approaches.Collaborate with engineering teams to deploy ML models in production environments, ensuring scalability and reliability.Develop custom training strategies, loss functions, and evaluation frameworks for domain-specific applications in telecommunications and government sectors.Optimize model performance, inference speed, and resource utilization for deployment across various hardware configurations.Conduct comprehensive model evaluation, A/B testing, and performance monitoring to ensure continuous improvement.Create technical documentation, research reports, and best practice guidelines for ML development processes.Mentor junior team members and contribute to the advancement of ML practices across the organization.Required QualificationsAdvanced degree in Computer Science, Machine Learning, Data Science, Mathematics, or a related field.4+ years of experience in machine learning engineering with a focus on deep learning and neural networks.Strong proficiency in Python and extensive experience with PyTorch, TensorFlow, and the HuggingFace ecosystem.Demonstrated expertise with Large Language Models including GPT, BERT, T5, LLaMA, and similar transformer-based architectures.Hands-on experience with diffusion models such as Stable Diffusion, DDPM, DDIM, or similar generative modeling techniques.Experience with ASR (Automatic Speech Recognition) systems and speech processing pipelines, including models like Whisper, Wav2Vec2, or similar technologies.Solid understanding of natural language processing techniques, including tokenization, embeddings, attention mechanisms, and sequence modeling.Experience with parameter-efficient fine-tuning methods, including LoRA, QLoRA, AdaLoRA, and other PEFT techniques.Knowledge of distributed training, model parallelism, and large-scale data processing frameworks.Familiarity with MLOps practices, model deployment, and production monitoring systems.Strong mathematical foundation in statistics, linear algebra, and optimization theory.Technical SkillsProgramming Languages: Expert-level Python; proficiency in C/C++, JavaScript, or Julia for performance optimization.Deep Learning Frameworks: Advanced experience with PyTorch, TensorFlow, JAX, and HuggingFace Transformers.LLM Technologies: Extensive knowledge of transformer architectures, attention mechanisms, tokenizers, and model scaling techniques.Generative Models: Hands-on experience with diffusion models, VAEs, GANs, and other generative modeling approaches.Fine-Tuning Techniques: Proficiency with LoRA, QLoRA, prefix tuning, prompt tuning, adapters, and instruction tuning methodologies.NLP Libraries: Experience with spaCy, NLTK, Gensim, sentence-transformers, and domain-specific NLP tools.Speech Processing: Familiarity with librosa, torchaudio, SpeechBrain, and audio processing pipelines.Model Optimization: Knowledge of quantization, pruning, distillation, ONNX, TensorRT, and inference optimization.Data Processing: Expertise in pandas, NumPy, Apache Spark, Dask, and distributed computing frameworks.MLOps Tools: Experience with Docker, Kubernetes, MLflow, Weights & Biases, Ray, and cloud ML platforms.Proficient in version control using Git, DVC, and collaborative ML development workflows.Preferred QualificationsExperience with multimodal learning combining text, audio, and visual data modalities.Knowledge of reinforcement learning from human feedback (RLHF) and constitutional AI approaches.Familiarity with federated learning and privacy-preserving machine learning techniques.Experience with real-time inference systems and streaming data processing.Understanding of model interpretability, explainable AI, and bias detection methodologies.Knowledge of graph neural networks and knowledge graph embedding techniques.Experience with AutoML frameworks and neural architecture search (NAS).Contributions to open-source ML projects or published research in top-tier conferences.Experience with edge deployment and mobile optimization for ML models.Understanding of cybersecurity applications and adversarial machine learning.Personal AttributesExceptional problem-solving skills and ability to approach complex ML challenges from first principles.Strong research mindset with the ability to stay current with the rapidly evolving ML/AI landscape.Excellent communication skills, both written and verbal, with the ability to explain complex concepts to diverse audiences.Self-driven and proactive, with a passion for pushing the boundaries of machine learning technology.Adaptable and open-minded, able to work effectively across different problem domains and data modalities.Collaborative team player with experience working in cross-functional environments.Strong attention to detail with commitment to reproducible and ethical AI practices.Additional InformationLanguage: Fluent oral and written English is mandatory.Good interpersonal skillsExcellent organizational skillsTeam player but able to work on own initiativeGood written and oral communication skillsEnthusiastic, self-starter and highly self-motivatedAppreciation of cultural differencesAttention to detailTravel: Occasional travel may be required for conferences, client meetings, or inter-office collaboration.We offer a very competitive package with excellent benefits and the opportunity to work on cutting-edgetechnologies that shape the future of telecommunications and data analytics. If you are a visionary technologist passionate about advancing the state-of-the-art in machine learning and making a global impact through innovative AI solutions, we want to hear from you!