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Principal Engineer

3 months ago

Remote, IN Noodle.ai Full time

ROLES AND RESPONSIBILITIES :

- Lead the design, development, and deployment of machine learning models and algorithms for supply chain optimization, with a focus on time series forecasting.

- Architect and implement end-to-end ML pipelines, ensuring robust and scalable deployment in production environments.

- Collaborate with cross-functional teams, including product managers, data engineers, and software developers, to integrate ML models into enterprise software products.

- Explore and apply generative AI and agent-based models to solve complex supply chain problems and enhance product features.

- Mentor and guide junior engineers, fostering a culture of innovation and excellence within the team.

- Stay abreast of the latest advancements in AI/ML technologies and apply them to improve existing products and develop new solutions.

- Conduct thorough evaluations and validations of models, ensuring high accuracy and performance.

- Engage with stakeholders to understand business needs and translate them into technical requirements in the product backlog.

MUST HAVE :

- 8+ years of experience with a significant focus on developing, deploying & supporting AI solutions in production environments.

- Proven experience in building enterprise software products for B2B businesses, particularly in the supply chain domain.

- Good understanding of Generics, OOPs concepts & Design Patterns

- Solid engineering and coding skills. Ability to write high-performance production quality code in Python

- Proficiency with ML libraries and frameworks (e.g., Pandas, TensorFlow, PyTorch, scikit-learn).

- Strong expertise in time series forecasting using stat, ML, DL and foundation models

- Experience of working on processing time series data employing techniques such as decomposition, clustering, outlier detection & treatment

- Exposure to generative AI models and agent architectures on platforms such as AWS Bedrock, Crew AI, Mosaic/Databricks, Azure

- Experience of working with modern data architectures, including data lakes and data warehouses, having leveraged one or more of the frameworks such as Airbyte, Airflow, Dagster, AWS Glue, Snowflake, Databricks, DBT

- Hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying ML models in cloud environments.

- Excellent problem-solving skills and the ability to work independently as well as in a collaborative team environment.

- Effective communication skills, with the ability to convey complex technical concepts to non-technical stakeholders

(ref:hirist.tech)