AI/ML Ops Architect

Must-Have Technical/Functional Skills

• Programming Languages: Python, Java 
• Agentic AI : Google ADK, A2A, LangChain/LangGraph, CrewAI, Semantic Kernel/Autogen and Open AI Agentic SDK 
• Tool Integration: Gemini Tools, Custom MCP tools
• Machine Learning Frameworks: Experience with TensorFlow, PyTorch and AutoML. 
• Generative AI: Hands-on experience with generative AI models, RAG (Retrieval-Augmented Generation) architecture, and Natural Language Processing (NLP). 
• Cloud Platforms: Google Cloud Platform (GCP), Vertex AI and Kubeflow
• Data Engineering: Proficiency in data preprocessing and feature engineering. 
• Version Control: Experience with GitHub for version control. 
• Data Science Practices: Skills in building models, testing/validation, and deployment. 
• Databases: DB2, Oracle, BigQuery, BigData, Cassandra, PostGRES
• Collaboration: Experience working in an Agile framework. 
• RAG Architecture: Experience with data ingestion, data retrieval, and data generation using optimal methods such as hybrid search. 
• Good to Have: Knowledge in GPU programming, GPU profiling, GPU optimizations & TensorRT. 

Functional Skills

• Experience working with customers in Retail Domain.
• Knowledge in Retail Pricing functionalities is a plus.

Roles & Responsibilities

• Meet with IT and Business teams to understand the requirements and opportunities
• Architect systems for AI/ML, agent-based AI workflows. 
• Lead the design and development of AI/ML, MLOps & Agentic AI solutions
• Develop and optimize ML models, pipelines, and orchestration logic
• Deploy AI/ML Models, LLM-based pipelines, agent orchestration, and vector-based memory systems
• Work closely with data scientists, ML engineers, DevOps, and software engineers to ensure seamless integration and deployment of solutions
• Drive technical strategy, tooling, and infrastructure decisions. 
• Provides management with timely communication on status & utilizes appropriate tools and/or develops custom solutions as required to meet objectives. 
• Identifies areas for process improvement. 
• Maintains appropriate communication within the team and across various teams (i.e., internal and external).

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