Artificial Intelligence Engineer
Ironbook AIKurnool, Republic Of India, IN14 days ago
Job descriptionBuild scalable data pipelines using Glue, EMR, Kinesis, or Lambda. Implement MLOps workflows using SageMaker Pipelines, Model Registry, MLflow (if applicable), and CI / CD. Monitor and optimize model performance, drift detection, retraining triggers. Integrate models with applications via REST APIs / async APIs. Work with microservices using Python (FastAPI), Node.Js, or similar. Build inference endpoints optimized for low latency and cost efficiency. Architect and deploy AI workloads following AWS Well-Architected best practices. Optimize compute, storage, and networking for high performance and cost efficiency. Implement security, IAM policies, data encryption, and compliance practices. 5+ years of ML / AI engineering experience, preferably in production environments. Strong expertise with : AWS SageMaker (training, inference, Pipelines, Model Monitor, Debugger). Amazon Bedrock (LLMs, embeddings, fine-tuning or instruction tuning). Feature Store , SageMaker JumpStart , Batch Transform . Solid experience with deep learning frameworks : PyTorch , TensorFlow , Hugging Face , LangChain (optional but preferred). Experience building LLM agents, automation workflows, or RAG-based systems. Strong in Python (mandatory) Experience with FastAPI, microservices, containerized ML workloads Experience with Git, Docker, CI / CD pipelines Good understanding of data modeling, ETL / ELT concepts Experience with Glue, Athena, Kinesis, Redshift, or equivalent Strong hands-on with : Lambda ECS / EKS (nice to have) API Gateway CloudWatch IAM
Job Description : AI Engineer (AWS AI Stack)
Location : Remote (India)
Experience : 5+ Years
Availability : Immediate Joiners Preferred
Employment Type : Full-time
About the Role
We are looking for a highly skilled AI Engineer with strong hands-on experience across the AWS AI / ML ecosystem . You will design, build, and deploy AI systems, collaborate with cross-functional teams, and contribute to scalable, production-grade solutions using modern AWS-native tooling.
Key Responsibilities
AI / ML Solution Development
- Build, deploy, and optimize machine learning models on AWS using SageMaker, Bedrock, Lambda, EC2, ECR, and Step Functions.
- Develop end-to-end ML pipelines (training, evaluation, deployment, monitoring).
- Implement vector search, embeddings pipelines, and LLM-based applications using Amazon Bedrock or open-source models.
- Build RAG (Retrieval-Augmented Generation) workflows using AWS services such as OpenSearch / Aurora / DynamoDB.
Data Engineering & MLOps
Backend & Integration
Cloud Architecture & Optimization
Required Skills & Experience
Core AI / ML Skills
Programming
Data Engineering
Cloud & DevOps
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Artificial Intelligence Engineer • Kurnool, Republic Of India, IN
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