Gen AI Engineer/Architect

The Role

Responsibilities:

• Define and drive the AI/ML architecture and roadmap, including both traditional machine learning and Generative AI (GenAI) use cases.
• Design comprehensive end-to-end AI solutions covering data ingestion, feature engineering, model training, inference pipelines, and monitoring frameworks.
• Lead the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks, utilizing tools such as LangChain, LangGraph, or similar.
• Develop and deliver cutting-edge AI/ML solutions, incorporating genetic AI techniques, innovative design principles, and scalable deployment strategies.
• Gain a good understanding of traditional AI/ML approaches and leverage this knowledge to create robust, hybrid solutions.
• Collaborate with business stakeholders to translate requirements into scalable AI-driven technical solutions.
• Evaluate and select appropriate AI/ML tools, cloud services, frameworks, and libraries based on use case needs and industry best practices.
• Ensure models adhere to governance, security, explainability, and regulatory compliance, embedding ethical AI principles into system design.
• Guide engineering teams in the implementation of AI components, emphasizing scalability, reliability, and performance optimization.
• Partner with DevOps teams to establish CI/CD pipelines for AI, including model versioning, deployment automation, and ongoing A/B testing.
• Keep abreast of the latest industry research, breakthroughs, and emerging trends in AI, including tracing frameworks, LLM observability, and other innovative areas, recommending adoption of best practices and solutions.

Requirements:

• Proven experience 10+ years, excel in leading AI/ML architecture and strategy in enterprise environments.
• Strong expertise in designing and deploying large-scale AI/ML solutions, including LLMs, RAG frameworks, and genetic AI techniques.
• Experience with AI/ML tools and frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, or similar.
• Agentic AI experience: design, develop, and deliver tracing frameworks and LLM observability solutions.
• Deep understanding of data workflows, feature engineering, model training, evaluation, and deployment.
• Good understanding of traditional AI/ML concepts, alongside expertise in generative AI and related frameworks.
• Hands-on experience with AI/ML model observability, tracing frameworks, and monitoring solutions.
• Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform) and services tailored for AI deployment.
• Familiarity with model governance, security, explainability, and ethical AI standards.
• Experience in developing CI/CD pipelines for AI/ML, including model versioning, monitoring, and performance tuning.
• Strong problem-solving, communication, and stakeholder management skills.

Preferred, but not required:

• Advanced degree (Ph.D., Master’s) in Computer Science, Data Science, AI, or related fields.
• Publications or practical contributions to AI research and open-source projects.
• Experience working in regulated industries or environments requiring compliance and governance.
• Familiarity with project management and Agile practices.