Brainvire
Website:
brainvire.com
Job details:
Job Title: AI Architect
Experience: 7-12 years
About the Role
We are looking for an experienced AI Architect to design, implement, and scale enterprise grade AI solutions across industries including BFSI, Retail, and Manufacturing. The role requires strong technical depth in AI/ML, Generative AI, and emerging Agentic AI systems along with the ability to define architecture standards and collaborate closely with pre-sales and delivery teams. This is a hands-on architecture role involving solution design, governance, and enterprise AI implementation, and autonomous AI workflow orchestration. It may require travelling across multiple office locations such as USA, UAE, Saudi Arabia, Singapore etc.
Key Responsibilities
1. AI Solution Architecture
• Design end-to-end AI and ML architectures (data ingestion -> model training ->
deployment -> monitoring).
• Architect GenAI solutions using LLMs (RAG, fine-tuning, prompt engineering).
• Design and implement Agentic AI systems capable of autonomous decision-making
and multi-step task execution.
• Define integration patterns with ERP, CRM, workflow engines, and enterprise
systems.
• Create scalable and secure AI deployment models (Cloud / Hybrid).
• Establish orchestration frameworks for multi-agent collaboration and tool execution.
2. Generative AI, LLM & Agentic AI Implementation
• Build and deploy LLM-powered applications.
• Design RAG pipelines using vector databases.
• Implement autonomous AI agents using frameworks such as LangGraph, CrewAI,
AutoGen, or similar.
• Architect multi-agent workflows with memory management and tool invocation.
• Work with Azure OpenAI / AWS Bedrock / OpenAI APIs.
• Optimize prompts and reduce hallucination risks.
• Implement guardrails, policy engines, and response validation mechanisms.
• Enable AI systems to integrate with APIs, databases, and enterprise tools
dynamically.
3. Data, MLOps & LLMOps
• Define data pipelines and feature engineering strategies.
• Implement model versioning and lifecycle management.
• Establish monitoring frameworks (drift detection, performance tracking).
• Monitor agent performance, autonomy boundaries, and decision audit trails.
• Collaborate with DevOps teams for CI/CD integration.
• Define evaluation metrics for autonomous agents (task success rate, reasoning
validation, error handling).
4. Governance, Security & AI Autonomy Controls
• Define AI governance standards.
• Ensure data privacy and compliance (PII handling, role-based access).
• Design secure AI environments aligned with enterprise IT policies.
• Implement human-in-the-loop mechanisms for agent oversight.
• Define autonomy thresholds and escalation protocols.
• Ensure traceability and explainability in AI-driven decisions.
Required Skills
Technical Skills
• Strong experience in Python (AI/ML stack)
• Hands-on with ML frameworks (TensorFlow / PyTorch / Scikit-learn)
• Experience with LLM frameworks (LangChain / LlamaIndex)
• Experience designing Agentic AI systems and multi-agent orchestration
• Familiarity with frameworks such as LangGraph, CrewAI, AutoGen, or equivalent
• Experience with Vector Databases (Pinecone / FAISS / Weaviate / Azure AI Search)
• Cloud AI exposure (Azure / AWS / GCP)
• API design and microservices architecture
• Database knowledge (SQL / NoSQL)
Generative & Agentic AI Experience (Must Have)
• RAG implementation
• Prompt engineering
• Embedding pipelines
• Model evaluation and response validation
• Designing autonomous agents with memory, planning, and tool usage
• Experience implementing reasoning workflows and agent decision tracking
Good to Have
• Experience with RPA + AI integration
• Knowledge of Power Platform / Copilot integrations
• Experience in BFSI, Manufacturing, Healthcare, Retail domain
• Understanding of AI compliance frameworks
• Exposure to workflow orchestration tools and automation engines
Click on Apply to know more.