Description
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.
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).
(ref:hirist.tech)
