Sr. AI Architect

Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team.

Role Overview

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.

Key Responsibilities

AI & GenAI Architecture

• 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.

Agentic AI & Autonomous Systems

• 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:
  • LangGraph
  • CrewAI
  • AutoGen
  • Semantic Kernel
  • OpenAI Agents SDK
  • LangChain
• Implement agent collaboration, human-in-the-loop workflows, and agent monitoring mechanisms.

RAG Engineering

• 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/OpenSearch

AI Coding Platforms & Developer Productivity

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

LLMOps & Platform Engineering

• 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.

Technical Leadership

• 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.

Required Qualifications

Education

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

Experience

• 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.

Technical Skills

Generative AI & LLMs

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

RAG Technologies

• LangChain
• LlamaIndex
• Vector databases
• Embeddings
• Semantic Search
• Knowledge Graphs
• Hybrid Retrieval

Agentic AI

• LangGraph
• CrewAI
• AutoGen
• Semantic Kernel
• MCP (Model Context Protocol)
• Tool Calling
• Function Calling
• Agent Memory Architectures

Programming Languages

• Python (Expert)
• JavaScript / TypeScript
• C#
• Java (Preferred)

Cloud Platforms

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

DevOps & MLOps

• Docker
• Kubernetes
• GitHub Actions
• Jenkins
• Terraform
• MLflow
• Weights & Biases

Databases

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

Preferred Certifications

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