AI Engineer - Lead/Architect

Role overview

Education and experience

• Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• At least 8 years of experience designing and architecting scalable enterprise AI solutions.
• Experience in enterprise solution architecture and customer-facing consulting roles.

Technical qualifications

• Expert Python proficiency; strong experience with PyTorch, TensorFlow, Hugging Face Transformers, Scikit-learn, FastAPI, and REST APIs.
• Hands-on experience with LLMs, prompt engineering, RAG, fine-tuning, model evaluation, AI agents, multi-agent systems, function calling, and MCP.
• Experience with one or more of LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel, and with vector databases such as Pinecone, Azure AI Search, ChromaDB, Weaviate, Milvus, or FAISS.
• Experience with cloud platforms including Microsoft Azure, Azure OpenAI, Azure AI Foundry, Azure Machine Learning, AWS Bedrock, Amazon SageMaker, or Google Vertex AI.
• Experience with Docker, Kubernetes, Git, CI/CD, MLflow, Azure DevOps, GitHub Actions, MLOps, and LLMOps.

Responsibilities

• Design and architect scalable AI, Generative AI, and Agentic AI solutions and build cloud-native, secure, scalable AI platforms and APIs.
• Lead technical design discussions, architecture reviews, and solution planning; mentor AI engineers, data scientists, and full-stack teams.
• Establish engineering standards, coding best practices, AI governance frameworks, and technology roadmaps.
• Build production-ready AI solutions using MLOps and LLMOps practices; optimize model performance, latency, scalability, and costs.
• Collaborate with product owners, business analysts, customers, and delivery teams to turn business challenges into AI solutions.
• Lead proofs of concept, customer workshops, and solution demonstrations; ensure AI systems are deployed, monitored, and continuously improved.
• Implement data privacy, security, explainability, ethical AI practices, observability, evaluation frameworks, and ongoing model optimization.

Leadership and preferred certifications

• Strong leadership, mentoring, analytical, and problem-solving skills; excellent communication, stakeholder management, presentation, and consulting skills.
• Ability to drive innovation, influence technical strategy, and deliver customer-focused AI solutions.
• Azure AI Engineer, Azure AI Foundry, AWS Machine Learning, or Google Cloud AI certifications are highly desirable.
• Exposure to AI security, governance, Responsible AI, and AI compliance frameworks is noted.