• US Citizen

• Green Card

• Corp-Corp

• W2-Permanent

• W2-Contract

• Contract to Hire

• Consulting/Contract

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 15th Apr 2025

• Collaborate with other data scientists, data engineers and DevOps engineers to help build and deploy models using SageMaker in a hybrid environment 

• Coordinate the build and automations for the entire MLOps pipeline including data and features, model (re)developments, deployment and ongoing monitoring of inference endpoints and model performance 

• Implement automated monitoring and alerting systems to detect and remediate potential issues 

proactively

• Look for opportunities to optimize timelines, resource utilizations and resiliency of end-to-end MLOps process 

• Collaborate for the development and integration of customized LLMs to enhance data analysis, natural language understanding, and generation tasks for agentic systems 

• Stay updated on the latest developments, explore and experiment to push boundaries and contribute to team and intellectual property development 

Must have technical skills (Min 8yrs of exp required in all of the below skills) 

• Python and PySpark proficient 

• Statistical analysis with data cleaning and augmentation experience 

• Strong footing on ML algorithms and their suitability for varied use cases 

• Deep learning and NLP experience (TensorFlow/PyTorch, BERT/GPT-3, etc.) 

• AWS SageMaker and additional AWS services (Lambda, StepFunctions, etc.) 

• Fine-tuning LLMs 

• SageMaker pipelines 

• Infrastructure-as-a-code (IaaC) 

• CI/CD 

• Model Monitoring 

• Explainable AI (XAI) 

Education/Certifications:

• AWS Certified Machine Learning – Specialty 

• AWS Certified DevOps Engineer – Professional 

• Other Cloud Solution Provider (CSP) certifications in these areas will also count 

• Additional Data Science and LLM focused certification will be a plus