AI/ML
- Posted a month ago
- Be among the first 20 applicants
Job Description
Key Responsibilities:
- Data Science Consultant with machine learning and Python experience
- Knowledge on end to end architecture of Data Science and machine learning project
- Experience in productionizing machine learning models and guide development team for implementing machine learning use cases
- The candidate should provide technology consulting for defining right Data Science and ML use cases
Technical Requirements:
- Data collection profiling EDA data preparation
- AI Model development tuning validation
- Visualization tools e
- g
- PowerBI tableau
- Present findings to business users project management teams
- Propose ML based solution approaches and estimates for new use cases
- Contribute to AI based modules in Infosys solutions development
- Explore new advances in AI continuously and execute PoCs
- Mentoring guide other team members
- ML algorithms
- AI domains NLP speech computer vision
- Supervised Unsupervised Reinforcement learning
- Tools for data analysis auto ML model deployment and scaling
- Knowledge of datasets
- Programming Python
- Databases
- Knowledge of cloud platforms Azure AWS GCP
Preferred Skills:
Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->generative ai,Technology->AI-Data science->PYTHONMore Info
About Company
Skills:
Pytorch, Python, GPU-based training experiments, reward design, multilingual or Indic-language models, JAX Flax, distributed training, reinforcement learning, knowledge distillation, model compression, post-training open-weight LLMs, supervised fine-tuning, preference optimization
Skills:
Scalability, Sql, Ml, Model Building, Deep Learning, Predictive Modelling, AWS, Python, Azure, Nlp, Hypothesis Testing, Data Cleaning, LLMs, Feature Engineering, Unsupervised Machine Learning, Supervised Machine Learning, Prompt Engineering, Ai, Operationalization, Experimentation Setup, Vector Databases, Transformer Architectures, RAG Applications
Skills:
computational science , theoretical physics , Artificial Intelligence, Control Algorithms, Software Development, Machine Learning, Simulation-Driven Engineering, Physics-Informed Neural Networks, Data Pipelines, Statistical Modeling, Systems engineering
Skills:
Ml, Cloud Migration, Software Engineering, multi-agent systems, Generative AI, application transformation, LLMs, Ai, AI-assisted application modernization, Security, Agentic AI, platform upgrades, responsible AI practices, Governance, observability, RAG architectures, autonomous agents