Machine Learning Engineer

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.

Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field.
8+ 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).