- 15+ years of software engineering/technology experience, including substantial hands-on experience architecting and engineering enterprise AI/ML/GenAI systems.

- Solid foundation in machine learning, including supervised and unsupervised learning, feature engineering, model selection, evaluation, experimentation and optimization.

- Strong deep learning expertise with practical experience in neural network architectures such as transformers, CNNs, sequence models and representation/embedding learning.

- Strong expertise in
Generative AI, LLMs, RAG, AI Agents and Agentic AI,

including tool use, orchestration, memory/context strategies and evaluation.

- Hands-on proficiency in

Python and modern AI/ML frameworks such as PyTorch, TensorFlow/Keras, scikit-learn and related model development libraries.

- Proven experience deploying AI/ML/GenAI solutions to production, including real-time and batch inference, API/model serving, scalability, resilience and performance optimization.

- Strong experience with MLOps/LLMOps, including experiment tracking, model/prompt versioning, CI/CD, model registries, automated testing/evaluation, monitoring, drift detection and lifecycle management.

- Experience with AWS, Azure and/or GCP, including AI/ML platforms and services such as Azure Machine Learning/Azure OpenAI,



AWS SageMaker/Bedrock or Google Vertex AI.

- Strong understanding of embeddings, vector databases, retrieval techniques, prompt/context engineering, LLM evaluation and grounding/citation approaches.

- Experience with data and feature pipelines, distributed processing and the data engineering patterns required to support production AI systems.

- Experience with Docker, Kubernetes, Git, CI/CD, APIs/microservices and modern software engineering practices for production AI applications.

- Strong understanding of AI security, privacy, governance, explainability, bias/fairness, model risk and responsible AI.

- Proven experience building enterprise AI platforms, reusable accelerators, shared AI services or AI Centers of Excellence.

- Ability to make sound architecture trade-offs across classical ML, deep learning and GenAI approaches based on accuracy, latency, cost, maintainability, data availability and business value.

- Strong retail, digital commerce or customer experience domain experience preferred.

- Excellent communication, consulting, stakeholder management and client-facing skills, with the ability to engage effectively with both CXO-level stakeholders and hands-on engineering teams.

📌 AI Technical Architect (retail domain expert) (Hyderabad)
🏢 Talentquell
📍 Hyderabad