AI Architect

Responsibilities:

AI & Machine Learning Architecture

• Architect, design, and guide the implementation of enterprise grade AI and ML solutions that solve complex business problems and power intelligent applications.
• Lead collaboration with data scientists, ML engineers, platform engineers, and software teams to integrate AI capabilities into products, platforms, and enterprise workflows.
• Provide hands-on technical leadership in Python , including proficiency with ML, GenAI, and LLM frameworks (TensorFlow, PyTorch, HuggingFace, LangChain, etc.).
• Oversee end-to-end data preparation, feature engineering, vectorization, and model development , ensuring data quality and readiness for LLMs and Generative AI workloads.
• Continuously evaluate emerging advancements in AI, ML, LLMs, Agentic Systems , and incorporate relevant innovations into architectural roadmaps and solution designs.
• Optimize AI and Generative AI models for performance, latency, scalability, observability, and cost efficiency , leveraging cloud-native and distributed compute environments.
• Conduct and drive technical design reviews, code reviews, and architecture assessments to ensure high standards of quality and engineering rigor.
• Diagnose, troubleshoot, and resolve complex AI system issues to maintain the reliability, accuracy, and security of production AI applications.

Generative AI, Copilots & Coding Assistants

• Design and architect enterprise-wide capabilities leveraging AI coding assistants, copilots, and developer productivity tools (e.g., GitHub Copilot, Azure AI Studio assistants).
• Lead the development of extensions, plugins, and integrations for IDEs like Visual Studio Code, IntelliJ IDEA, and Eclipse to embed AI-driven developer workflows.
• Apply deep understanding of prompt engineering, LLM behaviour, context-window optimization, fine‑tuning, and agentic workflows to enhance solution performance.
• Design, train, and evaluate custom generative models , exploring architectures such as GANs, VAEs, diffusion models, and transformer-based architectures .
• Architect pipelines for data acquisition, cleaning, text chunking, retrieval augmentation (RAG) , and ingestion into vector stores.
• Integrate enterprise solutions with AI platforms and APIs such as OpenAI, Azure OpenAI, LangChain, and other orchestration frameworks.
• Design and maintain the LLM data layer using technologies like Azure Cosmos DB, Azure AI Search , vector stores, and embedding indexes.
• Develop agentic automation solutions using frameworks such as LangChain, LlamaIndex, Semantic Kernel, and AutoGen , and integrate them with enterprise systems.
• Apply strong DevOps practices using CI/CD pipelines, GitHub Actions, Azure DevOps , and modern testing frameworks to maintain robust and automated AI development workflows.
• Contribute to or lead copilot initiatives across domains such as application development, customer support, operations, and productivity automation .

Prompt Engineering

• Design, test, and refine high‑quality prompts , prompt templates, and generative AI interaction models tailored to organizational use cases.
• Work closely with product, content, and data teams to align prompt behaviour with business outcomes, brand voice, and user experience requirements.
• Continuously improve prompt structures to enhance accuracy, relevance, safety, and contextual understanding within AI/LLM systems.
• Experiment with novel prompting strategies—chain-of-thought, retrieval-augmented prompts, tool-augmented prompts, system message optimization—to improve AI performance.
• Build and maintain an enterprise prompt library , ensuring versioning, governance, reuse, and integration into automated workflows and applications.
• Participate in fine-tuning and reinforcement learning processes by designing ground‑truth prompts, evaluation prompts, and tuning datasets .
• Diagnose and refine prompts that yield suboptimal or incorrect results, ensuring predictable and aligned LLM behaviour across use cases.

Must Have Skills:

• Strong hands-on programming expertise in at least one modern language such as Python, C#, Java, or JavaScript , with the ability to guide engineering teams on best practices.
• Deep proficiency with AI/ML frameworks and libraries including TensorFlow, PyTorch, and Keras , with the ability to architect scalable training and inference pipelines.
• Advanced understanding of Generative AI architectures , including GANs, VAEs, transformer-based models , and experience applying these techniques to real-world enterprise use cases.
• Practical experience using AI Coding Assistants
• Hands-on expertise with Agentic AI platforms , including Microsoft Copilot Agents, AutoGPT, CrewAI, Anthropic Claude , and other autonomous workflow systems.
• Strong command of Cloud & MLOps platforms , such as AWS SageMaker, Azure Machine Learning, Google AI Platform , including experience building automated model deployment, monitoring, and retraining pipelines.
• Solid experience architecting solutions on major cloud platforms (AWS, Azure, GCP ), including familiarity with cloud-native AI, data, and security services.
• Proven ability to mentor, guide, and provide architectural direction to junior and mid-level AI/ML engineers across multiple projects.
• Ability to conduct AI/ML and Generative AI code reviews , ensuring adherence to architectural standards, performance benchmarks, and responsible AI practices.
• Strong analytical and problem‑solving skills, including the ability to rapidly diagnose and resolve complex issues in AI systems, ML pipelines, and application runtimes .
• Excellent communication skills, capable of translating complex AI concepts for both technical teams and business stakeholders .
• Experience defining enterprise AI architectures, reference models, reusable design patterns , and establishing organization-wide architectural standards.
• Ability to define and implement AI governance frameworks , including Responsible AI guidelines, compliance controls, model guardrails, safety protocols , and risk-mitigation practices.