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.