We are looking for a Data Scientist with strong software engineering fundamentals and hands‑on experience deploying LLM‑powered systems into production. In this role, you will lead AI initiatives end‑to‑end—from proof of concept to secure, scalable enterprise deployment.

This is an excellent opportunity to work on cutting‑edge technology within a large global organization while contributing directly to high‑impact AI solutions.

Key Responsibilities

AI Solution Design & Development

• Design, develop, and deploy robust AI and Generative AI services from POC to full enterprise implementation.
• Build microservices, APIs, and data pipelines to support LLM‑based solutions.

Architecture & Scalability

• Architect scalable, secure, and high‑availability AI systems.
• Implement efficient model deployment workflows and distributed system designs.

CI/CD & MLOps Implementation

• Lead best practices in CI/CD and MLOps to automate model training, testing, performance monitoring, and deployment.
• Use modern orchestration tools to support end‑to‑end ML workflows.

Cross‑Functional Collaboration

• Work closely with AI Product Owners, data scientists, and engineering stakeholders to deliver production‑ready AI systems.
• Translate business requirements into technical designs and implementation plans.

Advanced AI Development

• Build and integrate multi‑agent systems and LLM‑based products to enhance business capabilities.
• Contribute to innovation initiatives within the AI engineering team.

Quality, Reliability & Compliance

• Ensure deployed AI systems meet high standards for observability, reliability, compliance, and security.

Required Qualifications

Technical Skills

• 5+ years of professional software engineering experience.
• Strong expertise in Python (mandatory).
• Proficiency in at least one additional language: Node.js or Java.
• Backend experience with frameworks such as FastAPI, Flask, or Django.
• Hands‑on experience building microservices architectures.

Infrastructure & MLOps

• Experience with Docker, Kubernetes, and orchestration tools: GitHub Actions, MLFlow, etc.
• Experience implementing CI/CD and MLOps pipelines in Azure ML, Databricks, or AWS.
• Strong understanding of ETL processes, API integrations, and model deployment workflows.

System Design & Architecture

• Knowledge of distributed systems, big‑data platforms, and scalable backend architectures.