Experienced AI/ML Engineer Needed

@irsolutions

I possess over 10 years of expertise in web and mobile development, specializing in AI/ML projects. I understand the need for a highly experienced AI/ML Engineer with 7+ years of experience like yourself to design, develop, and deploy machine learning models for real-time projects. My extensive background in AI/ML frameworks such as TensorFlow and PyTorch, combined with my strong Python knowledge, makes me the ideal candidate for your project in the AI/ML domain. In approaching your requirements, my strategic insight lies in optimizing machine learning models for superior performance and scalability. I have a proven track record in AI/ML development, implementing intelligent features that enhance user experiences, which aligns perfectly with your goal of delivering scalable AI solutions. With a focus on data analysis, preprocessing, and feature engineering, I am well-equipped to collaborate with your cross-functional teams to meet project requirements efficiently. I encourage you to take the next step and reach out to discuss the roadmap for your AI/ML project. I am confident that my expertise and past successes in similar domains will prove valuable in delivering exceptional results for your project within the specified budget and timeline.

@nrajuu07

With over 7 years of expertise in the field, I am well-equipped to handle your project's needs, and more. My skills span from statistical and quantitative analysis, exploratory data analysis, and data transformation to Machine Learning techniques such as regression, classification, clustering, and ensemble learning. Additionally, my proficiency in designing and optimizing CNNs, RNNs, LSTM, GRU models aligns perfectly with your requirements. Having a strong background in developing NLP models for text classification and sentiment analysis further adds to my list of capabilities. Not only am I trained extensively in the usage of popular AI/ML frameworks like TensorFlow and PyTorch as mentioned in your JD but I additionally have hands-on experience with Scikit-learn and Keras – giving me versatility when it comes to model optimization and scaling solutions. Most importantly, I approach every task with a 'problem-solving' mindset which combined with my good communication skills ensures smooth collaboration with cross-functional teams. Choose me for your project and you can expect not just task completion but also timely delivery of scalable AI solutions that suit your project's requirements. Looking forward to hearing from you!

@muneeb6767

I’m a seasoned AI/ML engineer with 7+ years in Python, TensorFlow, and PyTorch—experienced in deploying scalable ML/DL models, performing feature engineering, and working across NLP and computer vision projects, delivering robust solutions that meet complex requirements.

@alizain721

You asked for a 7+ year AI/ML engineer comfortable with real-time projects and TensorFlow/PyTorch — that’s my daily work. I’ve deployed NLP and computer vision models into production that serve live traffic and coordinate with cross-functional teams. One thing you didn’t mention explicitly is production model monitoring and automated retraining. Real-time systems fail most often from drift and latency, not initial accuracy, so a plan for monitoring and retrain triggers is critical. I recently built a real-time object detection pipeline in PyTorch, containerized with Docker, served on Kubernetes at 200 RPS, cut inference latency to 40 ms, and added drift detection that auto-started retraining when data distributions shifted. That reduced incidents by 60 percent. My approach: quickly audit your data and current models, prototype in TensorFlow or PyTorch to meet accuracy and latency targets, then productionize with optimized serving (ONNX/TensorRT if needed), CI/CD, and monitoring for drift and performance. I write clean, testable Python and use scikit-learn for feature work. Would you be available for a 15-minute call to review your current pipeline and the biggest pain point right now: latency, data quality, or deployment? Regards

@vishal1145

Your model deployment strategy will fail if you're not handling inference latency and memory constraints upfront. I've seen teams build accurate models that crash in production because they didn't architect for real-time serving at scale. Before designing the ML pipeline, I need clarity on two things: What's your current inference volume - are we talking 100 predictions per second or 10,000? And what's your deployment target - edge devices with limited memory or cloud infrastructure where we can optimize for throughput? Here's the technical approach: - PYTHON + TENSORFLOW/PYTORCH: Build production-grade models with quantization and pruning to reduce inference time from 500ms to under 50ms without sacrificing accuracy. - SCIKIT-LEARN + FEATURE ENGINEERING: Implement automated feature pipelines with validation checks that prevent data drift and maintain model performance over time. - DEEP LEARNING OPTIMIZATION: Use TensorRT or ONNX Runtime to convert models for GPU acceleration, handling 10x more requests per instance. - DATA MINING + PREPROCESSING: Design ETL workflows with Apache Spark that process millions of records daily while maintaining data quality through automated anomaly detection. - MODEL MONITORING: Set up MLflow tracking and A/B testing infrastructure to catch performance degradation before it impacts users. I've built ML systems for 3 companies that scaled from prototype to production serving 50K+ daily predictions. I don't take projects where the deployment architecture isn't defined upfront - let's schedule a 20-minute technical call to discuss your infrastructure constraints and performance requirements before committing to timelines.

@SkillCrafts

I have spent the last eight years architecting production-grade AI systems, moving complex models from experimental notebooks to high-availability cloud environments. Having recently spearheaded the optimization of large-scale transformer models for real-time inference, I understand the delicate balance between predictive accuracy and computational efficiency your project likely demands. My deep background in neural network design and the MLOps lifecycle ensures I can provide the senior-level technical leadership and hands-on execution required to deliver a robust, scalable solution that aligns with your specific business objectives. My approach begins with a comprehensive audit of your data pipeline and architecture to identify bottlenecks in feature engineering or gradient convergence. I utilize frameworks like PyTorch or TensorFlow for core development, employing advanced hyperparameter optimization via Optuna to maximize performance. Beyond the model itself, I focus on the deployment ecosystem—implementing rigorous validation to prevent data leakage and containerizing the solution using Docker and Kubernetes to ensure seamless scalability, versioning, and real-time monitoring in your production environment. To ensure we are aligned, are you looking to refine an existing architecture for better generalization, or are we building a custom solution from the ground up? Additionally, do you have specific constraints regarding inference latency or hardware targets for the design phase? I would welcome a brief chat to explore your roadmap and discuss how my eight years of experience can help you achieve your technical goals with precision.

@kend27

Hi, I'm a Philippines-based AI/ML Engineer with 7+ years of production experience — this role matches my background closely. I built large-scale NLP and Computer Vision pipelines handling millions of real-world interactions — covering deep learning model development, real-time inference optimisation, and end-to-end deployment. and I designed and deployed ML-powered microservices on AWS, reducing processing errors by 48% and improving pipeline throughput by 40%. Technically I work across: — Python, PyTorch, TensorFlow, Scikit-learn, Hugging Face — NLP, Computer Vision, deep learning architectures — Feature engineering, model optimisation, scalable ML pipelines — Docker, AWS, CI/CD for production deployment I deliver working models with measurable outcomes — not just experiments. Happy to share specific project details or discuss your current AI/ML requirements. What does the first project involve?

@azwa04

Hi, I'M TOP RATED ON UPWORK I can easily DO your work IN 24 HOURS, DM me now to get started, PRICE NEGOTIABLE 100% Work satisfaction is provided

@Hemantt841

Delivering scalable and efficient AI solutions requires not just technical proficiency but a deep understanding of model lifecycle management, from data preprocessing to deployment and optimization. The challenge of integrating machine learning models into real-time environments demands a strategic approach to balancing model accuracy with computational efficiency, ensuring both performance and responsiveness under production loads. Leveraging a strong command of Python and expertise in frameworks such as TensorFlow, PyTorch, and Scikit-learn, the focus will be on crafting robust machine learning and deep learning models tailored to your project’s specific needs. Proficiency in data analysis and feature engineering will be applied to extract meaningful insights and enhance model performance. Additionally, experience in NLP and computer vision domains will allow for flexible adaptation depending on your project’s requirements, while a collaborative mindset ensures seamless integration with cross-functional teams. Committed to delivering high-quality, scalable AI models within agreed timelines, the approach emphasizes transparent communication and iterative feedback to align outcomes with your objectives. Ready to contribute to your real-time AI projects with precision and efficiency, I invite you to discuss how we can achieve your goals together and drive impactful results.

@UzeeTech

With 7+ years of experience, I have developed a comprehensive understanding of AI/ML models throughout the deployment cycle. My expertise lies in not just developing and deploying machine learning models but also in performing data analysis, preprocessing, and feature engineering to ensure robust models. Additionally, I am well-versed in using AI/ML frameworks like TensorFlow and PyTorch for optimal model performance and scalability. Being a full-stack developer proficient in Python—a prerequisite for effective ML tasks—I can ensure that the entire pipeline from backend architecture to polished frontend stays cohesive and error-free, ensuring scalable outcomes. To supplement this, my knowledge extends beyond just the tech stack with fluency in NLP, Computer Vision, and other similar domains essential in AI development. Furthermore, past clients appreciate my communication skills as I provide daily updates and realistic timelines to avoid any surprises along the way. I pride myself on delivering clean, documented code that is easily testable and maintainable—a quality your future developers will undoubtedly appreciate! Lastly, I understand the value of time; 98% of my projects have been completed before or on-time. So if you’re looking for a meticulous approach blended with extensive expertise, let's discuss your project further so we can build something truly impactful together!

@CyberSasu

8 years exp!! Hello! I'm Saswata, and I would like to propose my 7+ years of expertise as an AI/ML engineer for your project. As depicted in my profile, I have a formidable grasp on Python and possess hands-on experience with frameworks like TensorFlow and PyTorch. This repertoire, coupled with my firm understanding of Machine Learning and Deep Learning, makes me a strong contender for this role. Moreover, my profound comprehension of NLP, Computer Vision, and similar domains will aid me as we tackle the real-time tasks lined up for this engagement. My commitment to optimization ensures performance scalabilities while maintaining quality standards throughout the process. In addition to deep technical skills, collaborative problem-solving and communication are areas where I excel. Assisting cross-functional teams aligning with project requirements is second nature to me; strengthened by strong proficiency in English. Choose me and let's create an automated world marvel together!

@indreshpnk

Hello, I understand you need an experienced AI/ML engineer to design, develop, and deploy scalable machine learning models for real-time applications. The goal is to deliver efficient, high-performance, and production-ready AI solutions. Here’s what I can provide: Development and deployment of ML/DL models using Python, TensorFlow, PyTorch, and Scikit-learn Data preprocessing, feature engineering, and optimization for improved model accuracy and scalability End-to-end support from model building to integration with real-time systems I bring over 4+ years of experience in Python, Machine Learning, and AI development, with hands-on work in data-driven applications and model optimization. I’ve worked on practical ML solutions focusing on performance, reliability, and clean implementation. Just to clarify a few things: What type of projects or domain (NLP, CV, etc.) will the models focus on? Do you require full-time collaboration or task-based engagement? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@ssldevmaster

I am an experienced AI/ML engineer with 7+ years designing, developing, and deploying scalable machine learning models. I will handle data preprocessing, feature engineering, and build models using TensorFlow or PyTorch for real-time, high-performance applications. I have expertise in NLP, computer vision, and deep learning, ensuring solutions are optimized, robust, and production-ready. Let’s discuss your project requirements I’m ready to start delivering results immediately.