Ml engineer signal processing / Jobs

We are seeking an experienced and visionary Principal Applied Data Scientist to lead the design, development, and deployment of cutting-edge AI and Machine Learning solutions. This role is ideal for a highly accomplished professional with deep expertise in Machine Learning, Large Language Models (LLMs), and Natural Language Processing (NLP), along with a proven track record of translating research into scalable, production-grade applications.

As a technical leader, you will work closely with cross-functional teams to solve complex business problems, define AI strategy, and mentor high-performing data science teams. You will drive innovation across the AI lifecycle—from problem formulation and model development to deployment, monitoring, and continuous optimization.

Requirements

Key Responsibilities

Lead the architecture, design, and implementation of advanced Machine Learning and AI solutions for enterprise-scale applications.
Build, fine-tune, and optimize Large Language Models (LLMs) for domain-specific use cases, including conversational AI, information retrieval, summarization, and intelligent automation.
Develop and enhance NLP pipelines for text classification, semantic search, entity recognition, sentiment analysis, document understanding, and knowledge extraction.
Design scalable ML systems using modern MLOps practices, ensuring reliability, reproducibility, and operational excellence.
Collaborate with engineering, product, and business stakeholders to identify AI opportunities and translate requirements into impactful solutions.
Evaluate emerging AI technologies, foundation models, and research trends to drive continuous innovation.
Establish best practices for experimentation, model evaluation, responsible AI, and performance optimization.
Mentor senior data scientists and engineers, providing technical leadership and fostering a culture of excellence and innovation.
Drive end-to-end deployment of AI models while ensuring scalability, security, and compliance with organizational standards.
Communicate technical concepts and strategic recommendations effectively to executive leadership and business stakeholders.

Required Skills & Qualifications

13–21 years of professional experience in Data Science, Artificial Intelligence, or Machine Learning, with significant experience leading complex AI initiatives.
Strong expertise in Machine Learning, including supervised, unsupervised, deep learning, and reinforcement learning techniques.
Extensive hands-on experience with Large Language Models (LLMs), prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), model evaluation, and AI agents.
Deep understanding of Natural Language Processing (NLP) concepts, transformer architectures, embeddings, and language understanding techniques.
Strong proficiency in Python and experience with frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, or similar ecosystems.
Experience with vector databases, model serving, distributed computing, and cloud-native AI platforms.
Solid understanding of MLOps, model lifecycle management, CI/CD for ML, and production deployment strategies.
Experience working with large-scale structured and unstructured datasets and modern data engineering practices.
Strong analytical, problem-solving, and communication skills with the ability to influence technical and business decisions.

Preferred Qualifications

PhD or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative discipline.
Contributions to AI research, patents, publications, or open-source initiatives are highly valued.
Experience leading enterprise AI transformation programs and mentoring large technical teams.
Familiarity with responsible AI frameworks, governance, explainability, and ethical AI practices.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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