Machine Learning Engineer (AWS Professional Services)

Job Description Role: Machine Learning Engineer with AWS Professional Services Experience: 8+ Years Work Mode: Remote Looking for someone who can join us immediately

As a

Machine Learning Engineer (MLE)

in

AWS Professional Services , you will work directly with enterprise customers to design, build, and deploy scalable ML and AI solutions on AWS. This is a customer-facing, hands-on technical role that combines deep ML expertise with cloud architecture, consulting, and delivery skills. You will help organizations operationalize AI/ML workloads, modernize data platforms, and accelerate innovation using AWS services.

Key Responsibilities Partner with customer stakeholders (data scientists, engineers, architects, executives) to define ML strategy and architecture. Design and implement end-to-end ML pipelines using AWS services such as: Amazon SageMaker AWS Lambda Amazon S3 Amazon EMR Amazon Bedrock Build and

deploy ML models

(supervised, unsupervised, deep learning, NLP, LLMs). Develop MLOps frameworks

(CI/CD for ML, model monitoring, feature stores). Lead workshops, architecture reviews, and proof-of-concept engagements. Provide best practices for security, cost optimization, scalability, and reliability. Contribute reusable assets, accelerators, and reference architectures. Mentor customer teams and internal AWS engineers.

Basic Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field. 6+ years of experience in: Machine learning engineering or data science Python and ML libraries (TensorFlow, PyTorch, Scikit-learn) Model deployment and productionization Experience with cloud platforms (AWS preferred). Strong understanding of: Feature engineering Model evaluation & experimentation Distributed training MLOps concepts Ability to travel to customer sites (varies by region).

Preferred Qualifications Experience delivering ML projects in consulting or customer-facing roles. Hands-on experience with: LLMs, generative AI, RAG architectures Real-time inference systems Data engineering pipelines (Spark, Kafka) AWS certifications (e.g., AWS Certified Machine Learning – Specialty). Strong communication and stakeholder management skills. Experience in regulated industries (finance, healthcare, public sector).

Key Competencies Customer obsession Ownership mindset Architectural thinking Bias for action Ability to operate in ambiguity Strong documentation and presentation skills

What Makes This Role Unique Exposure to diverse industries and cutting-edge AI use cases Direct impact on enterprise AI transformation Access to AWS internal tooling and ML specialists Opportunity to influence best practices at global scale