AI/ML Engineer - AWS SageMaker in Bombay, India - Jobeax
Vacancy description
AI/ML Engineer - AWS SageMaker in Bombay, भारत
Savishty
On-siteWork from the office
Bombay, भारत
About the Role:We are looking for an experienced AI/ML Engineer to develop and deploy machine learning solutions for a large-scale analytics https://jobeax.com/link/gFvV6IB1kex3UOLO Responsibilities:- Own and deliver multiple AI/ML use cases.- Design, train and deploy ML models using AWS SageMaker.- Monitor model performance, accuracy and reliability.- Analyze large-scale datasets and generate actionable insights.- Support advanced analytics and production ML use https://jobeax.com/link/5ULIIZT7CBMuYcGX Skills:- 5+ years of experience in AI/ML.- Strong hands-on experience with Python.- Strong experience with AWS SageMaker for training, deployment and monitoring.- Experience with Scikit-learn, TensorFlow/PyTorch, Pandas and NumPy.- Understanding of MLOps and production model deployment.- Experience taking ML models from development to https://jobeax.com/link/vrY4A0db2LymWKn5 to Have:- Oil & Gas domain experience.- Demand forecasting experience.- Supply Chain Analytics https://jobeax.com/link/yINR7yqmdZSwXPbz Location:- Mumbai - On-site (ref:hirist.tech)
... Integrate AI/ML capabilities into cloud workloads such as chatbots, automation and data insights Apply Generative AI services in AWS including Amazon Bedrock, SageMaker and OpenAI APIs Drive AI-driven automation in DevOps and migration processes Documented 5 to 8 years of professional experience in cloud engineering and migration ...
... resolve connectivity, access, deployment, and production issues. - SageMaker, AI Integration & Agentic Enablement - Integrate AWS data platforms with Amazon SageMaker for data preparation, feature engineering, model training, deployment, MLOps workflows, and inference-ready data products. - Support SageMaker Pipelines, Feature ...
... tools and libraries (Tableau, Power BI, Matplotlib, Seaborn).- Familiarity with big data platforms (Spark, Hadoop) and SQL databases.- Knowledge of cloud-based ML platforms (AWS SageMaker, Azure ML, GCP AI Platform).- Understanding of experimental design and A/B https://jobeax.com/link/U0J0g4FWpP0OjGT7 Testing Responsibilities ...
... meaningful insights. 4. Develop and integrate AI/ML models into applications and production environments. 5. Work with Natural Language Processing (NLP), Generative AI, Large Language Models (LLMs), or other AI technologies as required. 6. Perform data preprocessing, feature engineering, model training, and performance evaluation. ...
... ML workloads on AWS (SageMaker, EC2, EKS).- Perform benchmarking, fairness analysis, and performance evaluation across diverse datasets.- Collaborate with engineering and product teams to build scalable, low-latency biometric solutions.- Mentor engineers and participate in architecture and design https://jobeax.com/link/oZQFhqxglZrlg0Hz ...
... TensorFlow and PyTorch. Experience with LLMs, Generative AI, RAG architectures, vector databases, and NLP. - Hands-on experience with cloud AI platforms such as Azure AI, AWS SageMaker, or Google Vertex AI. - Knowledge of data engineering, Spark, Databricks, and data pipelines. - Experience with model deployment, MLOps, CI/CD, ...
... TensorFlow and PyTorch. Experience with LLMs, Generative AI, RAG architectures, vector databases, and NLP. - Hands-on experience with cloud AI platforms such as Azure AI, AWS SageMaker, or Google Vertex AI. - Knowledge of data engineering, Spark, Databricks, and data pipelines. - Experience with model deployment, MLOps, CI/CD, ...
... categorisation, release readiness for a software validation and DevOps organisation. Skills Must have Python, SQL, Git LLM application work: RAG, prompt engineering, AI agents, embeddings, vector databases, LLM evaluation LangChain or LangGraph, FastAPI ML fundamentals: PyTorch, TensorFlow or scikit-learn, model training and evaluation ...
... training pipelines that AI researchers and ML engineers use to take models from experiment to production. Own data versioning, validation, and reproducibility for ML workflows. Deploy and support production AI/ML systems. Build and maintain the pipelines that serve model training, testing, validation, deployment, and inference ...