C2C Role - AIML Architect - Charlotte, NC - Hybrid

Hi,

Hope you are doing well.

Please go through the following requirements and let me know with your updated resume if you are comfortable.

AIML Architect - Need Architect Profiles

Charlotte, NC – 3 - 4 days a week onsite

Long term Contract

Role Description: Key Responsibilities - GenAI, Agentic AI, FastAPI, pipeline building, ML and model deployment in SageMaker.

Professional experience in AI/ML engineering, data engineering, solution architecture, or related fields.
Expert-level proficiency in Python and ML frameworks (Scikit-learn, TensorFlow, PyTorch, H2O, XGBoost, etc.).
Strong hands-on experience with AWS SageMaker for architecting, training, tuning, deployment, and pipeline automation of enterprise ML solutions.
Strong knowledge of H2O.ai (Driverless AI or H2O3), AutoML frameworks, and enterprise ML workflows.
Proficient with MLflow for experiment tracking, model packaging, deployment, and lifecycle management.
Advanced experience with PySpark and distributed data processing.
Experience with AWS EMR for Spark cluster management and large-scale data transformations.
Solid understanding of MLOps concepts including CI/CD for ML, feature stores, monitoring, drift detection, and model governance.
Strong background in object-oriented programming, algorithm design, software engineering best practices, and scalable solution architecture.
Experience with Docker and containerized ML workloads.

Machine Learning Development

Design, build, and deploy robust ML models using Python and industry-standard ML frameworks (TensorFlow, PyTorch, Scikit‑learn, XGBoost, etc.).
Collaborate with data scientists to translate prototypes into production-ready systems.
Perform feature engineering, data preprocessing, model selection, hyperparameter tuning, and performance optimization.

MLOps & Productionalization

Develop and maintain ML pipelines using AWS SageMaker, MLflow, H2O.ai, and other automation tools.
Implement best practices for model versioning, lineage tracking, model performance monitoring, and retraining.
Set up CI/CD pipelines for ML services and automate deployment workflows.

Cloud & Distributed Systems

Architect and operate scalable ML workflows in AWS, including SageMaker, Step Functions, S3, ECR, CloudWatch, IAM, etc.
Build and optimize distributed data processing pipelines using PySpark and AWS EMR.
Ensure reliability, scalability, and cost efficiency of ML environments.

Data Engineering Integration

Work closely with data engineering teams to build robust data ingestion and transformation pipelines.
Improve data quality, reliability, and observability for ML use cases.
Heavy hands-on coding with PySpark, SQL, and Python-based ETL workflows.

Collaboration & Leadership

Provide technical mentorship and guidance to junior ML engineers and data scientists.
Lead architectural discussions and participate in design reviews.
Partner with cross-functional teams to scope and deliver ML-driven products.

Preferred Qualifications

Knowledge of Kubernetes (EKS) for ML deployment.
Experience implementing model monitoring systems (e.g., Neptune, SageMaker Model Monitor, custom solutions).
Familiarity with microservices, REST APIs, and event-driven architectures.
Experience with large language models (LLMs) and vector databases is a plus.

Thanks

Sathish Korapati

Technical Recruiter