Sr. AI Architect

We are seeking an experienced Senior AI Architect to lead the design, development, and deployment of next-generation Generative AI and Agentic AI solutions
. The ideal candidate will have deep expertise in Retrieval-Augmented Generation (RAG) pipelines, AI coding assistants such as Claude Code, OpenAI Codex, and Cline
, and experience building autonomous AI agents capable of reasoning, planning, and executing complex business workflows. This role requires a hands‑on technical leader who can define AI strategy, architect enterprise‑grade AI platforms, guide engineering teams, and drive innovation across GenAI, LLMOps, and intelligent agent ecosystems.

• Design and implement enterprise-scale Generative AI solutions using Large Language Models (LLMs).
• Architect advanced RAG-based systems incorporating vector databases, semantic search, enterprise knowledge repositories, and hybrid retrieval models.
• Define scalable AI architectures covering data ingestion, embeddings, retrieval, orchestration, guardrails, observability, and evaluation frameworks.
• Establish AI governance, security, compliance, and responsible AI practices.

• Design and develop multi-agent and Agentic AI solutions capable of planning, reasoning, tool usage, memory management, and workflow orchestration.
• Build autonomous agents using frameworks such as:
  • Lang Graph
  • CrewAI
  • Auto Gen
  • Semantic Kernel
  • OpenAI Agents SDK
  • Lang Chain
• Implement agent collaboration, human-in-the-loop workflows, and agent monitoring mechanisms.

• Architect and optimize:
  • Knowledge ingestion pipelines
  • Document chunking strategies
  • Embedding architectures
  • Query optimization
  • Re-ranking models
  • Hybrid search implementations
• Work with vector databases such as:
  • Pinecone
  • Weaviate
  • Qdrant
  • Chroma
  • Azure AI Search
  • Elasticsearch/Open Search

• Lead adoption and integration of AI-assisted development tools including:
  • Claude Code
  • OpenAI Codex
  • Cline
  • Git Hub Copilot
  • Cursor
• Define standards and best practices for AI-driven software engineering and code generation workflows.
• Architect AI-powered SDLC automation capabilities.

• Build robust CI/CD pipelines for AI applications.
• Implement:
  • Model evaluation frameworks
  • Prompt management
  • Experiment tracking
  • Cost optimization
  • AI observability
  • Production monitoring
• Establish enterprise LLMOps standards and governance models.

• Mentor architects, engineers, and AI specialists.
• Define technology roadmaps and AI strategy aligned with business objectives.
• Engage with stakeholders, product teams, and executive leadership to translate business challenges into AI solutions.
• Evaluate emerging AI technologies and recommend adoption strategies.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.

• 12 years of overall software engineering experience.
• 5 years in AI/ML solution architecture.
• 2 years building production-grade Generative AI applications.
• Proven experience delivering enterprise-scale AI platforms.

• GPT-4/5
• Claude
• Gemini
• Llama
• Mistral
• Deep Seek
• Open-source LLM ecosystems

• Lang Chain
• Llama Index
• Vector databases
• Embeddings
• Semantic Search
• Knowledge Graphs
• Hybrid Retrieval

• Lang Graph
• CrewAI
• Auto Gen
• Semantic Kernel
• MCP (Model Context Protocol)
• Tool Calling
• Function Calling
• Agent Memory Architectures

• Python (Expert)
• JavaScript / Type Script
• C#
• Java (Preferred)

• Microsoft Azure (Preferred)
• Azure AI Foundry
• Azure OpenAI
• AWS Bedrock
• Google Vertex AI

• Docker
• Kubernetes
• Git Hub Actions
• Jenkins
• Terraform
• MLflow
• Weights & Biases

• PostgreSQL
• MongoDB
• Cosmos DB
• Redis
• Neo4j
• Vector Databases

• Microsoft Certified:
  Azure AI Engineer Associate
• Azure Solutions Architect Expert
• AWS Machine Learning Specialty
• Google Professional Machine Learning Engineer
• Databricks Generative AI Certification