<p><p>We are looking for a skilled Machine Learning Engineer with expertise in AWS to design, build, and deploy scalable machine learning solutions.
The ideal candidate will have a strong background in ML model development, cloud deployment, and data engineering.
You will collaborate closely with data scientists, software engineers, and product teams to bring advanced ML-driven features into production systems.</p><br/><p><b>Key Responsibilities :</b></p><p><br/></p><p>- Design, develop, and deploy machine learning models and pipelines on AWS cloud services.</p><p><br/></p><p>- Work with large datasets to perform data cleaning, preprocessing, and feature engineering.</p><p><br/></p><p>- Implement end-to-end ML lifecycle including model training, validation, deployment, and monitoring.</p><p><br/></p><p>- Leverage AWS services such as SageMaker, Lambda, EC2, S3, Glue, EMR, and Redshift for ML solutions.</p><p><br/></p><p>- Optimize ML models for performance, scalability, and cost-efficiency in cloud environments.</p><p><br/></p><p>- Collaborate with data scientists to transition prototypes into production-grade solutions.</p><p><br/></p><p>- Build APIs and integrations to make ML models consumable for applications and business workflows.</p><p><br/></p><p>- Establish monitoring and logging for deployed ML models to ensure reliability and performance.</p><p><br/></p><p>- Stay updated with the latest ML frameworks, AWS services, and industry best practices.</p><br/><p><b>Required Qualifications & Skills :</b></p><p><br/></p><p>- Bachelors/Masters degree in Computer Science, Data Science, AI/ML, or related field.</p><p><br/></p><p>- 3 to 6 years of experience in machine learning engineering or applied ML.</p><p><br/></p><p>- Strong programming skills in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).</p><p><br/></p><p>- Hands-on expertise in AWS cloud services (SageMaker, S3, Lambda, Glue, EMR, Redshift, ECS/EKS).</p><p><br/></p><p>- Solid understanding of data pipelines, ETL processes, and big data technologies.</p><p><br/></p><p>- Experience with CI/CD for ML (MLOps) using AWS tools or alternatives.</p><p><br/></p><p>- Familiarity with containerization tools (Docker, Kubernetes) for ML deployment.</p><p><br/></p><p>- Strong knowledge of API development (REST, Flask, FastAPI).</p><p><br/></p><p>- Excellent problem-solving, analytical, and collaboration skills.</p><br/><p><b>Good to Have :</b></p><p><b><br/></b></p><p>- AWS Certification (e.g., AWS Certified Machine Learning Specialty).</p><p><br/></p><p>- Experience with deep learning models for NLP, Computer Vision, or Recommendation Systems.</p><p><br/></p><p>- Familiarity with streaming data solutions (Kafka, Kinesis).</p><p><br/></p><p>- Exposure to distributed training frameworks (Horovod, Ray).</p><p><br/></p><p>- Knowledge of security, compliance, and cost-optimization in cloud environments.</p><br/></p> (ref:hirist.tech)