AI ML Architect

Your Role:

Define and own the end-to-end architecture for enterprise-scale GenAI and AI solutions.

Design reference architectures, reusable patterns, and best practices for integrating GenAI into business applications.

Collaborate with domain experts, data scientists, and developers to align business needs with scalable AI architectures.

Select and evaluate LLMs, vector databases, and orchestration frameworks based on performance, compliance, and cost.

Architect RAG pipelines, agentic workflows, and multi-agent ecosystems for production-grade implementations.

Ensure security, privacy, and governance frameworks are embedded into AI systems from inception.

Drive adoption of cloud-native AI services (Azure OpenAI, AWS Bedrock) and ensure scalability and performance optimization.

Guide teams in model lifecycle management, including deployment, monitoring, retraining, and drift handling (MLOps).

Evaluate and recommend tools, frameworks, and protocols such as MCP, LangChain, and LangGraph for interoperability.

Stay updated with advancements in GenAI, AI regulations, and enterprise adoption trends, translating insights into strategic roadmaps.

Required Skills and Qualifications:

Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or a related field.

Proven experience as an AI/ML/GenAI Architect designing and implementing large-scale AI/ML systems.

Deep expertise in Python and ML/DL frameworks like PyTorch and TensorFlow.

Strong understanding of LLM architectures, fine-tuning methods (LoRA, PEFT, adapters), and deployment strategies.

Expertise in RAG pipelines, embeddings, and vector databases such as Elastic, Pinecone, and Milvus.

Familiarity with agentic GenAI systems including LangChain, LlamaIndex, AutoGen, Crew.ai, and LangGraph.

Knowledge of Model Context Protocol (MCP) for secure GenAI integrations.

Experience with cloud-native architecture (AWS, Azure) and container orchestration (Docker, Kubernetes).

Strong understanding of MLOps principles including CI/CD for ML, observability, retraining, and governance.

Ability to bridge business and technology, communicating complex AI strategies effectively to stakeholders.

Deep understanding of Responsible AI principles and integration of governance and ethical frameworks into solutions.

Bonus: Experience in enterprise-scale AI adoption within the Telecom industry.