Role Overview
Lead the design, development, and deployment of advanced AI systems
Build cutting-edge solutions across machine learning, NLP, generative AI, LLMs, and multi-agent orchestration
Drive innovation across our product portfolio by solving real-world problems
Key Responsibilities
Architect, build, and deploy production-grade AI/ML systems at scale
Design and develop RAG pipelines, agentic AI systems, and multi-agent orchestration solutions
Build intelligent automation flows and conversational AI agents using frameworks like LangGraph, LangChain, and Microsoft Copilot Studio
Develop time-series forecasting and anomaly detection models for real-world business use cases
Apply advanced prompt engineering and integrate Model Context Protocol (MCP) to connect AI agents with enterprise systems
Own the full AI project lifecycle - from data ingestion and preprocessing through model training, evaluation, deployment, and monitoring
Collaborate with data scientists, software engineers, and product managers to translate business needs into AI-powered solutions
Optimize model performance and ensure robustness, fairness, and explainability
Stay current with the latest AI/ML research and bring relevant advancements into our stack
Our Technology Landscape
Retrieval-Augmented Generation (RAG) pipelines
Agentic AI frameworks (e.g., LangGraph, LangChain)
Multi-agent orchestration systems
Model Context Protocol (MCP) integrations
Advanced prompt engineering
Time-series modeling, forecasting, and anomaly detection
Intelligent automation flows and workflow orchestration
Microsoft Copilot Studio for building and deploying custom copilots and conversational AI agents
Required Qualifications
Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field (PhD preferred)
5+ years of AI/ML engineering experience with a proven track record of shipping models to production
Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
Experience with cloud platforms (AWS, Azure) and MLOps tools
Strong understanding of data structures, algorithms, and software engineering principles
Solid grounding in software engineering best practices and system design
Experience designing and building automation workflows or process automation systems
Excellent problem-solving and communication skills