1000000 - 1500000 Indian Rupee - Yearly
Information Technology
Full-Time
Weekday
Overview
This role is for one of our clients
Industry: Technology, Information and Media
Seniority level: Mid-Senior level
Min Experience: 3 years
Location: Bengaluru
JobType: full-time
We are on the lookout for a Machine Intelligence Specialist to join our forward-thinking engineering team. This role is ideal for individuals passionate about building intelligent systems that transform raw data into real-world automation through advanced natural language processing (NLP), computer vision, and machine learning technologies.
You will lead initiatives involving deep learning, model deployment, and system optimization, contributing to high-impact products across domains. If you're energized by turning cutting-edge AI research into practical solutions, this is your opportunity to make a difference.
Your Mission
Design and Develop Intelligent Systems: Create robust machine learning models for tasks in NLP (e.g., text summarization, named entity recognition, semantic search) and computer vision (e.g., image classification, OCR, object detection).
Deep Learning Engineering: Build and optimize models using TensorFlow, Keras, or PyTorch, leveraging neural architectures such as CNNs, RNNs, LSTMs, and Transformers.
Collaborative Innovation: Partner with product, data, and engineering teams to align AI development with real business needs, ensuring seamless integration of intelligence into products and workflows.
Cloud-Native Deployment: Implement and scale ML models on AWS, Azure, or GCP using native services and containerization tools (Docker, Kubernetes).
AI at Scale: Drive performance tuning, data preprocessing, A/B testing, and continuous training to ensure accuracy and production reliability.
Model Lifecycle Ownership: Implement MLOps best practices, including CI/CD pipelines for ML, model versioning, and monitoring using tools like MLflow or SageMaker.
Ethical & Transparent AI: Prioritize explainability, fairness, and compliance throughout the model development lifecycle.
What You Bring
Essential Qualifications
3+ years of hands-on experience in designing, training, and deploying machine learning models.
Strong background in Python and ML libraries: TensorFlow, Keras, OpenCV, scikit-learn, Hugging Face Transformers, etc.
Experience in both NLP and computer vision use cases and toolkits.
Proven ability to deploy models using cloud-native AI tools on AWS, Azure, or GCP.
Familiarity with containerization (Docker) and orchestration (Kubernetes).
Solid foundation in mathematics, statistics, and deep learning algorithms.
Excellent communication skills and a collaborative mindset.
Bonus Points For
Experience with MLOps workflows and tools like MLflow, TFX, or Kubeflow.
Exposure to edge AI or streaming inference systems.
Understanding of responsible AI principles and data governance.
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