Visa sponsorship eligibility: No

Job Description:

• Complete hands-on Experience in Python programming, along with experience with popular AI/ML frameworks such as Tensorflow, Pytorch, scikit-learn, Langchain, and Llamaindex.
• Strong background in Machine learning models building and implementation.
• Hands-on experience in developing AI/ML/GenAI solutions using AWS services such as Sagemaker.
• Experience with search algorithms, indexing techniques, summarization, and retrieval models for effective information retrieval tasks.
• Hands-on Experience with RAG architecture and its applications in natural language processing tasks.
• Good Exposure to Agentic / Multi-agent framework.
• Hands-on Experience in end-to-end development of machine learning and deep learning techniques, like predictive modeling, applied machine learning, and natural language processing.
• Expertise in data engineering, such as preprocessing and cleaning large datasets efficiently using Python, PySpark, and other manipulation tools like Pandas and NumPy. Experience with techniques such as data normalization, feature engineering, and data generation.
• Experience with cloud computing principles and experience in deploying, scaling, and monitoring AI/ML/GenAI solutions on cloud platforms like AWS.
• Deploy and monitor ML solutions using AWS services such as Lambda, API gateway, and ECS, and monitor their performance using CloudWatch.
• Experience with Docker and containerization.
• Able to communicate complex technical concepts effectively to technical and non-technical stakeholders and collaborate with cross-functional teams.

Must Have:

• A Master's Degree in Computer Science and Engineering
• Minimum of 14 years of IT experience.
• Minimum of 7 experience as an ML engineer/data Scientist.
• Hands-on experience using Python and APIs like Flask/Django/fastAPI.
• Hands-on experience with tools such as Langchain, llamaidnex, and streamline.
• Hands-on experience with semi-structured and unstructured data.
• Must have implemented a use case using LLMs.
• Must have implemented a use case using prompt engineering and fine-tuning of LLMs using LoRA/ PEFT.
• Must have implemented a use case using RAG architecture. Multi-agent framework is an added advantage.