Artificial Intelligence Engineer
Ironbook AIHubli-Dharwad, 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 • Hubli-Dharwad, IN
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