Experience: 4+ Years
Technical Stack: Python, LangChain, PyTorch, Azure AI
Position Summary
We are seeking a Senior AI Engineer to design and deploy Agentic and Generative AI solutions that automate and optimize complex business processes. The successful candidate will transition innovative prototypes into scalable, production-grade applications, driving efficiency through advanced AI orchestration and autonomous system design.
Key Responsibilities
Architectural Design: Develop and optimize RAG pipelines and multi-agent systems to facilitate autonomous decision-making and collaboration.
Integration & Implementation: Utilize Model Context Protocol (MCP) and Python to integrate AI models with external systems and Master Data Management (MDM) platforms.
MLOps & Deployment: Build and maintain CI/CD pipelines via Azure AI Foundry for model training, testing, and real-time performance monitoring.
Cross-Functional Leadership: Collaborate with stakeholders to translate business requirements into technical AI use cases, including automated visual inspection and financial reporting.
Governance: Ensure all solutions adhere to enterprise standards for security, data privacy, and Ethical AI (fairness, transparency, and traceability).
Technical Qualifications
Programming: Mastery of Python (C# experience is a plus).
AI Frameworks: Professional proficiency in LangChain for orchestration and PyTorch for model development/fine-tuning.
Generative AI: Expert-level prompt engineering and experience in refining LLM performance for enterprise applications.
Infrastructure: Proven experience deploying scalable AI solutions on cloud platforms, specifically Azure or AWS.
Data Systems: Strong understanding of feature engineering, data management, and automated visualization techniques.
Core Competencies
Analytical Rigor: Ability to solve complex problems with data-driven, scalable logic.
Business Orientation: Commitment to delivering measurable ROI through process digitization.
Communication: Proficiency in conveying technical architectures to non-technical stakeholders.
Adaptability: Rapidly integrating emerging AI research and frameworks into existing ecosystems.
