Artificial Intelligence Engineer

About the Role

Datafusion Systems is a pioneering company revolutionizing voice and data capture and processing for telecommunications and government agencies worldwide. Their innovative IP and voice data stream analytics solutions handle billions of data points daily, directly influencing government operations. This role offers location flexibility while supporting an international customer base and propelling company growth.

Key Responsibilities

• Design, develop, and refine large language models (LLMs) tailored for NLP tasks such as text classification, entity extraction, sentiment analysis, and conversational AI.
• Create and enhance diffusion models to generate synthetic and multimodal data, improving training datasets.
• Build comprehensive machine learning workflows for text, speech recognition, and multimodal data processing.
• Implement advanced transformer architectures and attention mechanisms for diverse sequence modeling challenges.
• Research and incorporate innovative ML methods, including few-shot, transfer, and multitask learning strategies.
• Work collaboratively with engineering teams to deploy scalable and robust ML models in production.
• Develop specialized training techniques, loss functions, and evaluation systems for telecom and government applications.
• Improve model efficiency, inference speed, and resource consumption across hardware platforms.
• Conduct thorough model assessments, A/B testing, and ongoing performance monitoring for continuous enhancement.
• Prepare detailed technical documentation, research analyses, and best practice guidance for ML developments.
• Mentor junior staff and foster the advancement of ML methodologies within the organization.

Required Qualifications

• Advanced degree in Computer Science, Machine Learning, Data Science, Mathematics, or related fields.
• Over 4 years of professional experience in machine learning engineering emphasizing deep learning and neural networks.
• Expertise in Python programming and hands-on experience with PyTorch, TensorFlow, and HuggingFace tools.
• Proven skills with large language models such as GPT, BERT, T5, LLaMA, and similar transformer frameworks.
• Practical knowledge of diffusion models including Stable Diffusion, DDPM, DDIM, or analogous generative techniques.
• Experience building Automatic Speech Recognition (ASR) pipelines using models like Whisper and Wav2Vec2.
• Strong understanding of NLP elements such as tokenization, embeddings, attention, and sequence modeling.
• Familiarity with parameter-efficient fine-tuning techniques like LoRA, QLoRA, AdaLoRA, and other PEFT methods.
• Awareness of distributed training, model parallelism, and large-scale data frameworks.
• Experience with deployment, MLOps practices, and performance monitoring for production ML models.
• Solid foundation in statistics, linear algebra, and optimization theory.

Technical Skills

• Proficient in Python; additional skills in C/C++, JavaScript, or Julia for performance tuning.
• Deep learning frameworks experience: PyTorch, TensorFlow, JAX, HuggingFace Transformers.
• Expertise in transformer architectures, tokenizers, and scaling methods.
• Generative modeling knowledge including diffusion models, VAEs, and GANs.
• Familiarity with fine-tuning strategies: LoRA, QLoRA, prefix tuning, prompt tuning, adapters.
• Experience with NLP libraries such as spaCy, NLTK, Gensim, and sentence-transformers.
• Speech processing tools: librosa, torchaudio, SpeechBrain.
• Model optimization techniques like quantization, pruning, distillation, ONNX, TensorRT.
• Data handling using pandas, NumPy, Apache Spark, Dask, distributed computing.
• Experience with MLOps tools: Docker, Kubernetes, MLflow, W&B, Ray, cloud ML platforms.
• Proficient in Git, DVC, and collaborative workflows.

Preferred Attributes

• Experience with multimodal learning integrating text, audio, and visual data.
• Knowledge of reinforcement learning from human feedback and constitutional AI.
• Familiarity with federated learning and privacy-focused ML approaches.
• Hands-on experience with real-time inference and streaming data systems.
• Understanding model interpretability, explainability, and bias detection methods.
• Skills in graph neural networks and knowledge graph embeddings.
• Experience working with AutoML and neural architecture search.
• Contributions to open-source or publications in premier ML research forums.
• Expertise in edge deployment and mobile ML optimization.
• Familiarity with cybersecurity ML applications and adversarial machine learning.

Personal Qualities

• Strong problem-solving approach tackling complex ML problems from fundamentals.
• A research-driven mindset to stay abreast of fast-changing AI/ML fields.
• Excellent verbal and written communication to convey intricate ideas effectively.
• Self-motivated with a drive to innovate and enhance ML technologies.
• Adaptable to diverse challenges and data types across multiple domains.
• Collaborative team player experienced in cross-functional cooperation.
• Detail-oriented and committed to ethical, reproducible AI solutions.

Additional Details

• Fluent English communication skills, both spoken and written, are mandatory.
• Strong interpersonal and organizational capabilities.
• Independent worker yet cooperative in team settings.
• Self-starter with high enthusiasm and motivation.
• Aware and appreciative of cultural differences.
• Attention to detail crucial.
• Occasional travel may be required for conferences, client interactions, or office collaborations.

Compensation & Benefits

A highly competitive salary package is offered, along with attractive benefits and the chance to work on cutting-edge telecom and data analytics technologies that influence global communication futures.

If you are a visionary expert keen on advancing the ML state-of-the-art and eager to make a worldwide impact through innovative AI initiatives, we encourage you to apply.