Top Machine Learning Consulting Experts 2025

As an AI consultant and founder of Clearlead AI Consulting, I help companies fulfil their potential through advanced technology like AI, ML, NLP and Data Science. Philosophy: Use technology to deliver real business impact by thoroughly understanding and addressing genuine business needs. Education: - Ph.D. in Information Retrieval and Machine Learning - B.Sc. in Software Engineering Experience: - Over 20 years at the forefront of technology - Former Director of Data Science at UnitedHealth Group (Fortune 5 company) - Roles in data science and machine learning at large multinational companies - Early career in research centres delivering cutting-edge research for companies like Samsung and various startups Expertise: - Artificial Intelligence (AI) - Machine Learning (ML) - Data Science - Natural Language Processing (MLP) - Information Retrieval - Data Mining Services: - Technical services in AI/ML, Data Science, and NLP - Strategic and innovative AI consulting - Research-based services in AI

I am Ashwin Prakash Nalwade. I have expertise in Back-End Development, Artificial Intelligence [Machine Learning, Natural Language Processing, Computer Vision]. Experienced in developing data-driven solutions to solve exciting problems in the sub-fields of AI. I bring about 3 years of combined experience ranging from a Series E (late-stage) startup, to an emerging pre-seed/seed stage startup, complemented by research at NYU. In addition to my AI expertise, I am proficient in AWS, Kubernetes, HPC, and Docker, which I leverage to build scalable, efficient back-end systems. My skills in Data Engineering enable me to manage and process large datasets effectively, further enhancing AI applications. https://github.com/ashwinpn Google Scholar - https://scholar.google.com/citations?user=mHMnqNQAAAAJ&hl

Senior Machine Learning Engineer with a strong track record of building large-scale, production-grade AI systems in complex and high-stake environments. I specialize in designing end-to-end ML pipelines and developing data-centric solutions. My expertise lies in LLMs, NLP, Computer Vision, ML systems design, data engineering, and human-in-the-loop automation. I build systems that operate reliably at scale, reduce manual work, and empower teams to deliver faster, safer, and more consistent decisions globally. In his recent work, he's built and deployed multi-stage ML pipelines that combine lightweight models, structured LLM-based logic. Earlier, Dmitrii developed ML-driven quality-control systems and crowdsourcing frameworks that strengthened data quality, automated validation, and increased throughput for engineering teams. These systems became part of the internal processes of several high-traffic products, transforming manual workflows into efficient, measurable systems.

I’m a ML Engineer with over 3 years of experience in implementing AI and Machine Learning solutions in the QA industry. Proficient in leveraging Python libraries such as TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and OpenCV to drive intelligent test automation and data-driven decision-making for mobile and web applications. Demonstrated success in integrating advanced technologies like YOLO and Selenium to build robust, auto-healing frameworks and enhance test efficiency. Strong R&D background with a focus on innovation in Python-based networking, image processing, and data scraping. Adept at collaborating with cross-functional teams to deliver scalable, automated testing solutions that adapt to evolving application environments and improve software quality.

Seeking a challenging software development role in machine learning, artificial intelligence, deep learning, and/or computer vision. Domain experience includes computer vision modeling for brand / logo tracking in advertising & marketing, building AI-based cybersecurity pipelines, image-based machine learning for medical diagnostics, and genetics / computational biology. Comfortable designing novel foundational models (e.g. variational auto-encoders, image/text classification, generative adversarial networks) as well as fine-tuning existing models including LLMs. Personal website: https://colbymainard.github.io/

Accomplished machine learning engineer with a Masters in Data Analytics and extensive industry experience. Specializes in MLOps, showcasing proficiency in deploying and maintaining machine learning models in production environments, fostering seamless integration with existing systems. Deep understanding of search relevancy, having worked on projects to enhance the accuracy and efficiency of search algorithms, incorporating both traditional keyword-based and advanced natural language processing techniques. My expertise extends to recommendation systems, where I have developed and fine-tuned algorithms for personalized content delivery across the marketplace. Excelling not only in model building but also in managing the end-to-end process, from data collection and preprocessing to deployment and monitoring. Effective collaboration with cross-functional teams, bridging the gap between data science and IT to create efficient and scalable deployment pipelines.

SEO consultant helping B2B and SaaS companies grow from 6 to 8 figures with strategic SEO, AI optimization and content marketing. I specialize in driving qualified traffic, building authority, and turning organic search into a scalable growth channel.

Masters of Science Data Analytics graduate student at Georgia Tech. Undergraduate degree majoring in Electrical Engineering with minors in Computer Science and Mathematics. Currently hold a secret level security clearance. Skills: R, Google Analytics, Mixpanel, Python, MATLAB, Java, C++, HTML

Driven by a commitment to pushing the boundaries of AI and machine learning to create tangible and meaningful impacts in the tech world. I thrive on the ever-evolving nature of AI field and being a life-long learner.

Most AI conversations I have with CEOs and CFOs start the same way: "We know we need to do something with AI, but we're not sure what will actually move the business forward." That's where I come in. I help executives separate the signal from the noise. Which AI investments make sense for your business? What should you build first? How do you measure if it's working? And more importantly, how do you avoid spending millions on projects that end up as expensive demos? I run STX Next's AI and Data practice. We're a 20-year-old engineering company (started as a Python shop, now 200+ people across Poland and Mexico) that's delivered over 1,000 projects for mid-market and enterprise clients. Financial services, industrials, healthcare, energy. Companies that need systems that scale, not experiments that fade. My team builds data platforms and AI systems that connect to real business problems. We work on Snowflake, Databricks, AWS, Azure, but the technology stack matters less than understanding what problem you're solving and what success looks like. Before any project starts, I ask the same question: "What decision will this system help you make faster or better?" If we can't answer that clearly, we probably shouldn't build it.

I am Brian Tham, an Applied Artificial Intelligence undergraduate at Singapore Institute of Technology with a strong academic record and practical experience in AI research and development. My work focuses on advancing AI applications, particularly in the areas of model protection, computer vision, and machine learning. You may view my portfolio/website at https://tham.ai As a Research Student at Singapore Institute of Technology, I've contributed to significant work on AI model protection, specifically focusing on defending deep neural networks against model extraction attacks. My research encompasses model watermarking, adversarial robustness, and advanced defense mechanisms, with practical applications in medical imaging through UNet model implementation for brain MRI segmentation. My technical portfolio includes notable projects such as FoodVision, a real-time deep learning food detection system utilizing YOLOv8 technology, and a Speech Emotion Recognition system based on Wav2Vec2. I've also conducted comprehensive analyses of emerging technologies, including a detailed study of ransomware impacts across various industries and an evaluation of AI chatbot capabilities. Previously, I enhanced medical education at Lee Kong Chian School of Medicine, where my e-learning courseware development contributed to a 20% improvement in student test scores. My professional experience extends to the Ministry of Defence of Singapore, where I specialized in data processing automation and visualization using PowerBI and Excel. I hold multiple prestigious certifications, including Google Professional Machine Learning Engineer, Google Business Intelligence, and AI4I® – Proficiency in AI from AI Singapore. My technical expertise spans Python, machine learning frameworks (TensorFlow, PyTorch), and data analysis tools, complemented by a deep understanding of quantum computing, large language models, and autonomous vehicle technologies. This combination of academic excellence, practical research experience, and technical expertise positions me at the forefront of AI innovation and its real-world applications.

Arun is a manufacturing professional working in South Africa, focused on applying artificial intelligence to solve real operational problems in FMCG manufacturing. His work sits at the intersection of AI, production, maintenance, and sustainability, with hands-on experience in improving efficiency, reliability, and resource utilisation in high-speed manufacturing environments. He has exposure to operations across emerging markets, particularly Africa and India, and focuses on practical, data-driven solutions rather than theoretical AI models.

Luka Breitig is an AI Solutions Architect and technical educator specializing in end-to-end AI system design, from problem discovery to production deployment. His background spans psychology, management, and software engineering, giving him an uncommon ability to translate human-centered insights into robust technical architectures. He works with organizations to identify high-leverage use cases, design scalable AI infrastructure, implement solutions with modern tooling, and train internal teams for long-term autonomy. His approach blends strategic clarity with practical engineering discipline. He also builds and maintains open-source software, including Tymeslot, a full-featured scheduling platform built with Elixir and Phoenix LiveView, and writes about AI reliability, system behavior, and practical deployment concerns. As a conference speaker and educator, he focuses on demystifying complex concepts for developers and leaders, ensuring that AI systems are not only powerful but dependable, understandable, and aligned with real organizational needs.

My name is Matthew Redford and I founded DATA HIT in 2024 with the goal of providing an effective data and optimisation resource to small to medium sized businesses. Our primary service areas are consultancy, tracking implementation, conversation rate optimisation, and insights / reporting. With over 15 years of experience, I have gained a wealth of experience working in technical and marketing teams across a number of reputable agency and client side roles. Away from my main career path I have also founded a number of successful web-based businesses.

My primary research interest is in data-driven decision making under uncertainty for retail, supply chain, transportation, logistics, and service operations applications. The methodologies employed in my research range from data analytics and statistical machine learning to data-driven optimization. My Erdös Number is 3 since Aug 2020.

I am an entrepreneur and a passionate advocate for integrating AI into business operations. As a partner and Director of Operations at Designs by Marc Limited, I draw on a strong IT foundation that began college while I did a Bachelor's degree in Information Systems from Howard University. My experience includes managing the online training platform for the Office of Ethics and Compliance at Abbott Laboratories, ensuring effective and compliant digital solutions. I also expanded my business knowledge at Harvard Business School, focusing on change management and leadership development.

Mathematician turned AI-researcher using the world's largest supercomputer(s) to build, validate, and deploy AI models to areas of national interest including fundamental science, energy, health, and security.

Head of AI Foundry at Evergreen Insight Global, where I lead both engineering and sales teams building AI/ML-enabled software for Fortune 500 clients in healthcare, financial services, and biopharma. I specialize in zero-trust AI architectures, enterprise governance frameworks, and production-grade AI guardrails for regulated industries. I've scaled our AI Foundry team from 0 to 70 people while maintaining hands-on technical involvement in client deployments. Previously served as VP of Data & AI at Truist Financial. Academic credentials include Stanford, MIT, and BITS Pilani, with 15+ years of experience spanning corporate banking, AI/ML engineering, and enterprise software development. Recent research contributions include presenting at ICMLA 2025 and being invited as a session chair for their Generative AI track. My work focuses on practical enterprise AI deployment patterns and responsible AI implementation at scale. Available to comment on: enterprise AI strategy, AI governance and compliance, agentic AI systems, AI in regulated industries, building and scaling AI teams, and the gap between AI research and production deployment.

I am a machine learning engineer and mathematician. I specialize in deep learning, large language models (LLMs), machine learning operations, and non-convex optimization.