Engineer -Digitalization

We are seeking a talented and driven Digitalization Engineer to design, build, and deploy next-generation artificial intelligence solutions. In this role, you will bridge the gap between AI research and practical software products. You will contribute for integrating foundational machine learning models, deploying large language models (LLMs), and architecting autonomous agentic workflows to solve complex enterprise problems.

The ideal candidate must understand model architecture, data pipeline production, software infrastructure, and how to deliver scalable, cost-efficient solutions.

• AI System Development Build and deploy production-grade AI systems, connecting foundational models, internal databases, data lakes, and third-party APIs.
• Application Development: Design and implement advanced GenAI workflows using Retrieval-Augmented Generation (RAG) and multi-agent frameworks.
• Model Optimization & Integration: Fine-tune open-source models, execute prompt engineering, and build custom wrappers to maximize system efficiency.
• Data Engineering: Construct scalable and robust data preprocessing pipelines to clean, structure, and transform multi-modal datasets.
• MLOps & Lifecycles: Maintain end-to-end model serving infrastructure, focusing heavily on reducing latency, monitoring model drift, and optimizing resource costs.
• Cross-Functional Collaboration: Partner closely with Data Scientists, Software Developers, and Product Managers to align technical execution with business strategy.
• Ethics & Security: Establish robust safety guidelines regarding data governance, privacy compliance (GDPR/CCPA), and mitigation of model bias.

• Programming Languages: High proficiency in Python (core requirement) along with working knowledge of Java script.
• AI Frameworks & Libraries: In-depth hands-on experience with PyTorch or TensorFlow.
• Generative AI Ecosystems: good to have knowledge on orchestration toolsets like LangChain, LlamaIndex, AutoGen, or CrewAI.
• Cloud & Infrastructure: Proven track record deploying AI services to Azure utilizing Docker, Kubernetes, and serverless compute.
• Databases & Pipelines: Strong command over SQL, NoSQL systems, and handling big data tools.