AI/ML Architect

Role Overview

We are looking for a skilled AI/ML Architect to drive the development of enterprise-grade Artificial Intelligence, Machine Learning, and Generative AI solutions. The role involves defining technical architecture, selecting suitable technologies, and guiding engineering teams in building scalable, secure, and high-performing AI applications. The candidate should have strong experience in cloud-based AI platforms, LLM applications, RAG implementation, and production-grade ML deployment.

Key Responsibilities

• Work with clients and business teams to identify opportunities for AI adoption and convert requirements into technical solutions.

• Design end-to-end AI/ML architectures across AWS, Azure, or GCP.

• Develop GenAI applications using LLMs, LangChain, LlamaIndex, prompt engineering, and vector databases.

• Build RAG-based solutions for enterprise search, intelligent assistants, and document processing.

• Guide Python-based model development, API creation, data preparation, and model integration.

• Establish MLOps and LLMOps practices covering CI/CD, model deployment, monitoring, and version control.

• Lead architecture reviews, technical evaluations, proof of concepts, and solution implementation.

• Mentor technical teams and ensure adherence to security, governance, performance, and scalability standards.

• Oversee technical delivery and collaborate with stakeholders to ensure successful project execution.

Required Skills

• 7+ years of overall IT experience, with at least 3 years in AI/ML architecture and solution design.

• Strong expertise in Generative AI, LLMs, RAG, prompt engineering, and orchestration frameworks.

• Hands-on experience with LangChain, LlamaIndex, Hugging Face, and vector databases such as Pinecone, FAISS, or Weaviate.

• Proficiency in Python, PyTorch, TensorFlow, NumPy, and Pandas.

• Good understanding of machine learning, deep learning, NLP, and transformer-based models.

• Experience with cloud AI services such as AWS SageMaker, Google Vertex AI, or Azure Machine Learning.

• Knowledge of Docker, Kubernetes, MLOps, LLMOps, model monitoring, and automated deployment pipelines.

• Strong solution design, technical leadership, client communication, and stakeholder management skills.