Machine Learning Engineer

Responsibilities

• Minimum experience should be 8+ years.
• 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.

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 (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).