We are looking for a highly skilled and motivated ML Engineer to build and deploy Generative AI solutions for supply chain analytics, supporting our business partner in the software industry.

You will be working with data scientist groups and act as the bridge by building pipelines and workflows to process large scale data and productize GenAI based features, Retrieval Augmented Generation (RAG) systems using cutting edge cloud, big data and LLM technologies.

Job Description

● Develop “best-in-class” architecture, design and production artifacts by ensuring that the services and the components are: well-defined and modularized, secure, scalable, reliable, reusable, diagnosable, and actively monitored.

● Contribute to all phases of new developments including architecting, prototyping, productizing, testing and support.

● Adhere to and drive modern software engineering practices through code and design reviews, exercise DevOps principles to deploy, maintain and monitor production workflows and services to provide continuous delivery with high quality and actionable telemetry data.

● Play an active role on end-to-end ML lifecycle, starting from prototyping to the deployment.

● Design, develop, and maintain RESTful APIs and backend services to support AI and machine learning models, ensuring high availability and performance.

● Deploy Generative AI and Traditional Machine Learning models into production and ensure seamless integration, scalability, and monitoring.

● Collaborate with data scientists to refine prompts that guide AI models in generating accurate and relevant outputs, tailoring prompts.

● Mentor junior developers with coaching and implementing orientation processes.

Qualifications:

● Familiarity with LLMs and Generative AI technologies

● 5+ years of experience, including work on SaaS systems

● Bachelor’s degree or higher in a relevant field

● Strong coding skills in Python or other OO languages

● Hands-on experience with SQL and NoSQL databases

● Solid understanding of ML and data science concepts

● Proven experience developing and deploying end-to-end ML solutions

● Proficiency with major Python ML libraries (e.g., Pandas, scikit-learn, TensorFlow, PyTorch,mlflow, lightgbm, xgboost)

● Experience with Python backend development frameworks (e.g., Flask, Django, FastAPI).

●Exposure to LLMs and Generative AI

Nice to have qualifications:

• Experience with Deep Learning, NLP, LLMs, or Computer Vision
• Familiarity with Big Data and distributed processing platforms
• Understanding of supply chain analytics (forecasting, inventory, pricing)
• Basic knowledge of DevOps/MLOps/DataOps concepts
• Experience with cloud-native tools (containers, orchestration, serverless)
• Awareness of CI/CD practices and tools like Docker, Jenkins, Terraform

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