Principal Gen AI Engineer

About the Role

We're looking for a highly skilled and experienced
Principal GenAI/AI Engineer and Architect
to join our innovative team. The ideal candidate has over a decade of hands-on experience in the AI/GenAI space, with a strong focus on Generative AI and Agentic AI. This role requires a unique blend of deep technical expertise, architectural vision, and a proven ability to bridge the gap between AI research and practical, enterprise-grade applications.

You'll be responsible for architecting, developing, and deploying cutting-edge GenAI solutions, with a particular emphasis on integrating Python-based GenAI models and tools with our core .NET technology stack. A significant part of your role will involve leveraging
GitHub Copilot
and other AI-assisted development tools to enhance productivity and streamline workflows.The role requires complete hands on expertise and always be ready to code and lead with example

Key Responsibilities

• Architect and Design:
  Lead the design and implementation of scalable, reliable, and secure GenAI and Agentic AI systems. This includes architecting GenAI pipelines for data ingestion, model training, and production deployment.
• Agentic AI Development:
  Design and build autonomous AI agents that can perform complex, multi-step tasks. You'll leverage and develop with advanced agentic frameworks like
  LangChain
  and
  LangGraph
  to build stateful, multi-actor applications with complex, dynamic control flows and cyclical patterns.
• SLM and LLM Integration:
  Possess deep, practical knowledge of integrating Large Language Models (LLMs) and Small Language Models (SLMs). You'll be responsible for orchestrating these models, using SLMs for specialized, low-latency tasks and routing more complex queries to LLMs, thereby optimizing for cost, performance, and efficiency.
• GenAI Engineering:
  Develop, train, and fine-tune large language models (LLMs) and other generative models. Possess a strong understanding of concepts like Retrieval-Augmented Generation (RAG) and vector databases.
• Full-Stack Development:
  Drive hands-on development of .NET-based applications and projects, applying strong software engineering principles to build robust, maintainable, and efficient systems.
• AI Tool Analysis:
  Continuously research and analyze the latest AI and GenAI tools, platforms, and models available in the market. Provide expert recommendations on which technologies to adopt to solve specific business problems.
• Leadership and Mentorship:
  Provide technical leadership and mentorship to junior engineers, helping them grow their skills in GenAI and the use of AI-assisted development tools.
• Collaboration:
  Work closely with cross-functional teams, including data scientists, product managers, and business stakeholders, to translate business needs into technical GenAI solutions.

Required Skills and Qualifications

• 10+ years
  of professional experience in software engineering, with at least 5 years specifically in AI/GenAI.
• Deep expertise in Python
  , including proficiency with major GenAI frameworks such as PyTorch, TensorFlow, or Hugging Face.
• Expertise in .NET and Engineering Best Practices:
  Possess deep, hands-on expertise in the Microsoft .NET technology stack (e.g., C#, ASP.NET Core) and a strong command of software engineering best practices, including writing clean, maintainable code, unit testing, code reviews, and applying SOLID principles.
• Cloud-Native Architecture:
  Proficient in designing and building scalable, resilient, and cost-effective cloud-native applications, with expertise in microservices, containerization (Docker, Kubernetes), and serverless architectures on major cloud platforms.
• Demonstrable practical experience using Copilot
  for a wide range of development tasks in a professional setting, especially in a .NET context.
• Strong, hands-on experience with LangChain and LangGraph
  is a must. You should be able to demonstrate a practical understanding of building multi-agent workflows with state management, loops, and conditional logic.
• Proven good knowledge on SLM and LLM integration
  , including an understanding of model distillation, quantization, and hybrid architectures that leverage the strengths of both model types.
• Proven capability in architecting complex GenAI systems
  that are scalable, maintainable, and cost-efficient.
• Solid understanding of AI/GenAI concepts
  , including LLMs, RAG, and fine-tuning.
• Experience with cloud platforms
  such as Azure, AWS, or GCP.
• Excellent problem-solving, analytical, and communication skills.
• A self-learner
  who thrives on staying at the forefront of new technologies and emerging trends in the Generative AI space.

Preferred Qualifications:

• Bachelor's, Master's, or Ph.D. in Computer Science, AI, GenAI, or a related field.
• Experience with GenAI operations (GenAIOps) practices and tools (e.g., MLflow, Azure ML pipelines).
• Data Science certifications or experience
  in areas like data cleaning, feature engineering, and predictive modeling.
• Knowledge of distributed systems, microservices, and API design.
• Familiarity with conainerization technologies like Docker and Kubernetes.