- 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 :

1. Amazon SageMaker

2. AWS Lambda

3. Amazon S3

4. Amazon EMR

5. 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 :

1. Machine learning engineering or data science

2. Python and ML libraries (TensorFlow, PyTorch, Scikit-learn)

3. Model deployment and productionization

4. Experience with cloud platforms (AWS preferred).

Strong understanding of :

1. Feature engineering

2. Model evaluation & experimentation

3. Distributed training

4. MLOps concepts

5. 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 :

1. LLMs, generative AI, RAG architectures

2. Real-time inference systems

3. Data engineering pipelines (Spark, Kafka)

4. AWS certifications (e.g., AWS Certified Machine Learning Specialty).

- Strong communication and stakeholder management skills.

- Experience in regulated industries (finance, healthcare, public sector).