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AI Engineer

Litmus7 -
Bengaluru, Karnataka

Apply Now

Job details

2 hours ago

Qualifications

TensorFlow
Azure
Data mining
Statistics
Computer Science
PyTorch
Software deployment
Master's degree
AWS
Sentiment analytics
Bachelor's degree
Machine learning
Natural language processing
Agile
AI
Python

Full job description

Deep expertise in machine learning algorithms and techniques like supervised/unsupervised learning, deep learning, reinforcement learning, etc.
Solid experience in natural language processing (NLP) - language models, text generation, sentiment analysis, etc.
Proven understanding of generative AI concepts like text/image/audio synthesis, diffusion models, transformers, etc.
Expertise in Agentic AI and building real world applications using the same.
Experience working with Agentic AI frameworks like Langgraph, ADK, Autogen etc.
Hands-on experience developing and deploying generative AI applications (text generation, conversational AI, image synthesis, etc.)
Experience in MLOps and ML model deployment pipelines.
Proficiency in programming languages like Python and ML frameworks like TensorFlow, PyTorch, etc.
Knowledge of cloud platforms (AWS, GCP, Azure) and tools for scalable ML solution deployment
Experience in data processing, feature engineering, and model training on large datasets.
Familiarity with responsible AI practices, AI ethics, model governance and risk mitigation.
Understanding of software engineering best practices and applying them to ML systems.
Experience in an agile development environment.
Exposure in tools/framework to monitor and analyse ML model performance and data accuracy.
Strong problem-solving, analytical, and communication abilities.
Bachelor’s or master’s degree in computer science, AI, Statistics, Math or related fields.

Responsibilities:

Design, develop and optimize machine learning models for applications across different domains.
Build natural language processing pipelines for tasks like text generation, summarization, translation, etc.
Develop and deploy cutting-edge generative AI & Agentic AI applications.
Implement MLOps practices - model training, evaluation, deployment, monitoring, and maintenance.
Integrate machine learning capabilities into existing products or build new AI-powered applications.
Perform data mining, cleaning, preparation, and augmentation for training robust ML models.
Collaborate with cross-functional teams to translate AI requirements into technical implementations.
Ensure model performance, scalability, reliability.
Continuously research and implement state-of-the-art AI/ML algorithms and techniques.

#LI-SP1

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