? ----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/Qualified.
- AWS Certified Data Analytics – Specialty.

📌 AI/ML Engineer – AWS ProServe Engagements (Noida)
🏢 Innova ESI
📍 Noida