Hi,

We have open position fo ML Architect for Valuelabs

Role & responsibilities : Senior Machine Learning Engineer Computer Vision Experience 10+ years of industry experience in Machine Learning, Deep Learning, and Computer Vision with a proven track record of designing, building, and deploying production-grade AI solutions at scale.

Key Responsibilities - Design, develop, train, evaluate, and deploy production-grade machine learning and deep learning models for computer vision applications.

- Build end-to-end machine learning pipelines covering data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.
- Train deep neural networks from scratch on large-scale image datasets and optimize model architectures for accuracy, latency, scalability, and robustness.
- Develop computer vision solutions for image classification, object detection, segmentation, localization, image similarity, and feature extraction.
- Own the complete machine learning lifecycle, including experiment design, hyperparameter optimization, model versioning, model registry, reproducible training pipelines, and model performance monitoring.
- Design and optimize distributed training pipelines utilizing multiple GPUs and efficiently process large-scale datasets.
- Evaluate model performance using statistical methods, rigorous experimentation, and business-centric success metrics.
- Apply model explainability techniques to validate, interpret, and communicate model predictions.
- Build scalable training and inference pipelines using AWS SageMaker and other cloud-native services.
- Collaborate closely with Product Managers, Data Scientists, Machine Learning Engineers, Software Engineers, Data Engineers, QA teams, domain experts, and business stakeholders to deliver production-ready AI solutions.
- Drive continuous model improvements through hypothesis-driven experimentation, error analysis, performance optimization, and data-driven decision making.
- Lead and mentor Machine Learning Engineers, Data Scientists, and Software Engineers. Provide technical direction,



establish engineering best practices, conduct architecture and code reviews, and drive execution of large-scale machine learning initiatives.

Required Technical Skills Machine

Learning - Strong understanding of supervised and unsupervised learning algorithms.
- Hands-on experience with regression, classification, clustering, ensemble learning, decision trees, random forests, and gradient boosting algorithms such as XGBoost, LightGBM, and CatBoost.
- Strong foundation in probability, statistics, hypothesis testing, experimental design, and statistical inference.
- Experience with feature engineering, model evaluation, cross-validation, bias-variance analysis, model calibration, and hyperparameter optimization. Deep Learning & Computer Vision - Strong expertise in TensorFlow (mandatory).
- Extensive experience training deep learning models from scratch on large-scale image datasets.
- Strong understanding of convolutional neural networks and contemporary computer vision architectures.
- Experience with image classification, object detection, semantic segmentation, instance segmentation, localization, embeddings, feature extraction, and image similarity.
- Experience designing custom neural network architectures, optimization techniques, loss functions, and distributed deep learning workflows.

Production Machine

Learning (Mandatory) - Proven experience designing, deploying, and operating production machine learning systems.
- Experience building scalable machine learning training and inference pipelines.
- Experience with model registry, experiment tracking, model versioning, CI/CD for machine learning, and model monitoring.
- Experience designing data ingestion pipelines and managing large-scale datasets.




- Hands-on experience with distributed training and multi-GPU environments.
- Strong understanding of machine learning system scalability, performance optimization, and production reliability. Programming & Software Engineering - Expert-level Python programming skills with experience building production-quality machine learning applications, reusable libraries, and scalable data processing pipelines.
- Strong understanding of software engineering principles, object-oriented design, design patterns, testing, debugging, performance optimization, and version control.
- Experience writing modular, maintainable, and production-quality code.
- Strong SQL and data analysis skills. Cloud & Infrastructure - Hands-on experience with AWS SageMaker.
- Experience deploying machine learning workloads on AWS.
- Familiarity with cloud-native machine learning infrastructure and scalable training environments.

Preferred Qualifications - Experience in automobile insurance, collision repair, automotive AI, or related computer vision domains.

- Experience building enterprise-scale machine learning products serving production customers.
- Exposure to MLOps platforms, distributed computing, and large-scale machine learning infrastructure.
- Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, or a related quantitative discipline.

What We're Looking For We are looking for a highly experienced Machine Learning Engineer with deep expertise in Computer Vision and Deep Learning. The ideal candidate has a strong background in TensorFlow, production machine learning systems, distributed model training, and large-scale data processing. They should be comfortable leading technical initiatives, collaborating across engineering and product teams, mentoring engineers, and delivering reliable, scalable, production-grade machine learning solutions.

Preferred candidate profile : Strong candidate with Good experience in ML, DL, Gen AI, SQL, model architectures

Perks and benefits : Hybrid Mode, WFH, Remote, Free Food

📌 Data Scientist (Hyderabad)
🏢 ValueLabs
📍 Hyderabad