Job Description :

The AI Architect will lead the design and deployment of advanced AI systems spanning Machine Learning, Deep Learning, Generative AI, Agentic AI, and MCP-enabled dynamic architectures. The role involves building LLM-powered applications, autonomous multi-agent systems, and context-aware AI platforms on Microsoft Azure using scalable, cloud-native frameworks.

The candidate will drive multimodal intelligence initiatives integrating text, image, video analytics, and 2D-to-3D reconstruction for enterprise and industrial use cases. This position requires expertise in dynamic tool orchestration, adaptive reasoning systems, and production-grade MLOps practices. The ideal candidate combines deep technical excellence with strategic leadership to translate complex AI capabilities into measurable business impact.

Key Responsibilities :

- Translate business requirements into technical specifications, data models, and AI solution architectures ready for implementation.

- Develop multi-agent systems with structured role-based collaboration, agent-to-agent communication, and workflow orchestration.

- Build dynamic tool-using agents capable of runtime tool discovery, API invocation, database querying, and external system integration.

- Implement memory architectures (short-term, long-term, vector-based memory) to enable contextual continuity and learning across sessions.

- Develop Retrieval-Augmented Generation (RAG) pipelines integrating embeddings, vector databases, and contextual search for knowledge-grounded responses.

- Design, code, train, fine-tune, and deploy Machine Learning, Deep Learning, and Generative AI models using Python, PyTorch, TensorFlow, and related frameworks.

- Engineer structured prompts, function-calling workflows, guardrails, and evaluation pipelines to ensure reliable agent behavior.

- Develop AI models for 2D-to-3D reconstruction using depth estimation, point cloud processing, and neural rendering techniques.

- Develop multimodal AI pipelines integrating text, image, video, and structured data.

- Implement Model Context Protocol (MCP)-compatible connectors for standardized context sharing, tool interoperability, and modular AI integration.

- Develop adaptive reasoning frameworks with dynamic task decomposition, decision trees, and feedback-driven response refinement.

- Build event-driven and API-triggered agent workflows using Azure services and scalable backend architectures.

- Deploy and scale Agentic AI systems using Azure OpenAI, Azure ML, Docker, and Azure Kubernetes Service (AKS).

- Monitor, debug, and optimize agent performance including latency, hallucination reduction, cost efficiency, and reasoning accuracy.

- Implement safety mechanisms, access controls, logging, and responsible AI guardrails for enterprise-grade deployment.

- Create scalable data ingestion, preprocessing, and feature engineering pipelines using Azure Databricks, Spark, and distributed data platforms.

- Implement CI/CD pipelines, model versioning, automated testing, performance tuning, and monitoring using MLflow and Azure-native tools.

- Optimize GPU utilization, inference latency, and system scalability for high-volume enterprise workloads.

- Debug, refactor, and continuously improve AI models and pipelines to ensure production reliability and performance.

Required Skills :

- Strong expertise in Machine Learning, Deep Learning, Generative AI, and LLM fine-tuning using Python, PyTorch, and TensorFlow.

- Hands-on experience in Agentic AI including multi-agent orchestration, tool integration, memory architectures, dynamic reasoning workflows, and MCP-based context interoperability.

- Proven experience building RAG pipelines, embeddings, vector databases, and semantic search systems for enterprise GenAI applications.

- Advanced knowledge of Computer Vision, image processing, video analytics, and 2D-3D reconstruction techniques (depth estimation, point clouds, NeRF).

- Strong experience with Azure (Azure OpenAI, Azure ML, Databricks, AKS) and AWS (SageMaker, Bedrock, EKS) AI ecosystems.

- Expertise in scalable data engineering and MLOps including Spark, SQL, MLflow, CI/CD, Docker, Kubernetes, and production-grade cloud deployments.

Preferred Skills :

- Experience in AI, Data Science, Applied ML, Deep Learning, or Gen AI Engineering, Agentic AI, Image Processing, MCP.

- Strong experience with cloud platforms (Azure and/or AWS).

- Hands-on experience with big data and distributed systems (Spark, Kafka, Airflow).

- Experience designing production-grade GenAI and agent-based systems.

- Strong understanding of statistics, probability, optimization, and experimentation.

- Experience with NLP, Computer Vision, Time Series Forecasting, and Recommendation Systems.

- Ability to simplify complex AI concepts into clear business value propositions.

- Proven leadership, ownership, and mentoring capabilities.

- Comfortable working in fast-paced, ambiguous, and innovation-driven environments.