AI/ML Engineer – AWS ProServe Engagements

📝 Job Description----cloud-native AI/ML Engineer

🔑 Key Responsibilities

Technical Leadership

• Design and implement end-to-end ML solutions using AWS services (SageMaker, Bedrock, Q Business, Q Developer).
• Lead technical discussions with AWS ProServe teams and customer stakeholders.
• Architect scalable ML pipelines and data workflows on AWS.
• Develop and deploy generative AI applications using Amazon Bedrock and foundation models.
• Implement MLOps best practices leveraging AWS native tools.Customer Engagement
• Collaborate with AWS ProServe consultants on discovery and solution design.
• Present technical solutions and recommendations to customer teams.
• Conduct workshops, proof-of-concept demos, and provide technical mentorship.
• Support pre-sales activities with solution validation.Solution Development
• Build production-ready ML models and applications on AWS infrastructure.
• Develop custom algorithms and optimize ML workflows.
• Implement data engineering solutions using AWS Glue, EMR, and related services.
• Create automated testing and deployment pipelines for ML models.
• Ensure compliance with security, performance, and governance standards.Collaboration & Knowledge Sharing
• Partner with AWS ProServe delivery teams on execution.
• Participate in technical reviews and architecture discussions.
• Document best practices and reusable components.
• Contribute to internal knowledge base and training materials.
• Mentor junior engineers and share expertise.🎯 Qualifications

Experience

• 3–5 years of hands-on experience in machine learning and data science.
• Prior experience with enterprise customers or consulting environments.Soft Skills
• Strong communication and presentation abilities.
• Ability to translate business requirements into technical solutions.
• Experience in agile/scrum environments.
• Customer-focused mindset with consulting exposure.
• Analytical thinking and problem-solving skills.Technical Skills
• AWS Services: SageMaker, Bedrock, Lambda, EC2, S3, Glue, EMR, QuickSight.
• ML/AI Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers.
• Languages: Python (required), R, SQL, Java/Scala (preferred).
• Generative AI: LLMs, prompt engineering, RAG architectures.
• Data Engineering: ETL/ELT, data warehousing, real-time streaming.
• MLOps: CI/CD, model versioning, monitoring, deployment automation.
• Infrastructure: Docker, Kubernetes, IaC (CloudFormation/CDK).Certifications (Preferred)
• AWS Certified Machine Learning – Specialty.
• AWS Certified Solutions Architect – Associate/Professional.
• AWS Certified Data Analytics – Specialty.