Senior AI Engineer

Profile: Senior AI Engineer Exp: 6+ years Location: Pune/Bangalore/Noida Budget - 45 Lacs Job Summary: Experience 4+ years of experience designing, developing, and deploying ML models in production environments 1+ year of experience in areas such as recommendation systems, pattern recognition, NLP, or time series modeling Experience with production-grade Python (preferred), as well as Java or C/C++ Hands-on experience with large-scale software architecture, APIs, and model versioning systems Skills Expertise in Python and ML frameworks such as Py.

Torch, Tensor. Flow, or scikit-learn Proficient in cloud-based ML platforms (e.g., Azure ML, Google Cloud Platform, AWS Sage. Maker) Solid understanding of machine learning algorithms (e.g., classification, regression, SVMs, ARIMA, ensemble methods, deep learning, neural network) Strong foundation in probability theory and statistical modeling (generative and discriminative) Familiarity with DevOps/MLOps practices, CI/CD pipelines, GitHub Actions, Terraform Docker, and Kubernetes Ability to communicate technical concepts clearly to both technical and non-technical stakeholders Strong collaboration skills with cross-functional teams (engineering, analytics, product) Ability to independently manage tasks and thrive in a remote-first or hybrid environment Preferred Skills Experience in regulated industries (e.

g., finance, healthcare, insurance) Excellent communication and stakeholder engagement skills Strong understanding of deep learning architectures (e.g. CNNs, RNNs, Transformers, GANs) Strong in GPU based accelerating computing technologies (CUDA, Rapids, Ne. Mo, NIM, etc.) Proficiency in model evaluation, distributed training, and hyperparameter optimization Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search) Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes) Proficient in API and Microservices technologies Track records in large-scale, real-time AI/GenAI/ML database and solution technologies Background in responsible AI/ML, model interpretability, and fairness auditing.