Machine Learning Engineer; AWS Professional Services

Position: 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 Sage Maker
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 (Tensor Flow, 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