Reflections Info Systems - Lead AI/Agentic Platform Architect

Vista Info Systems; Ernakulam; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Vista Info Systems in India seeks a senior architect to design and lead agentic AI platforms, ensuring scalable, enterprise-grade solutions across the stack. You will define orchestration patterns, memory management, RAG architectures, and guide integration of LLMs, tools, and connectors with strong emphasis on security and governance.

Collaborate with engineering, product, and business stakeholders to translate requirements into technical roadmaps, while driving reliability, observability, and

Qualifications

• 5+ years of experience in software architecture, AI engineering, platform engineering, or related technical roles, including hands-on experience building LLM-powered and agentic AI systems.
• Strong understanding of agent orchestration frameworks, agent runtimes, multi-agent systems, and workflow automation.
• Hands-on experience with LLM integrations, prompt engineering, function calling, and tool orchestration.
• Experience designing memory, state management, context handling, and RAG-based architectures.
• Knowledge of vector databases, embeddings, semantic search, and retrieval mechanisms.
• Experience implementing AI guardrails, evaluation frameworks, and observability solutions.
• Strong understanding of AI security, governance, data privacy, and responsible AI principles.

Responsibilities

• Design and architect scalable, enterprise-grade agentic AI platforms and solutions.
• Define agent orchestration patterns, runtime architecture, workflows, and multi-agent communication mechanisms.
• Architect prompt engineering frameworks, tool calling, connectors, and model integration strategies.
• Design memory management, state persistence, and context management mechanisms.
• Evaluate and implement RAG architectures and vector database integrations wherever applicable.
• Design and integrate LLM-based applications using leading commercial and open-source models.
• Establish model integration patterns, routing, fallback mechanisms, and model selection strategies.
• Define reusable components and frameworks for agent development and deployment.
• Optimize model performance, latency, scalability, and token consumption.
• Define evaluation frameworks to measure agent accuracy, reliability, consistency, and performance.
• Establish mechanisms for testing prompts, tools, workflows, and end-to-end agent behaviour.
• Implement telemetry, monitoring, tracing, and observability for agentic systems.
• Define mechanisms to identify hallucinations, failures, and performance bottlenecks.
• Identify and address security and governance gaps in agentic AI architectures.
• Establish guardrails for responsible AI implementation, including access controls and data protection.
• Define policies for tool permissions, human-in-the-loop controls, and agent execution boundaries.
• Ensure appropriate compliance, auditability, and governance standards are incorporated into the architecture.
• Assess existing AI and agentic platform architectures and map technical dependencies.
• Identify architecture gaps, integration challenges, scalability risks, and opportunities for improvement.
• Recommend target-state architecture and implementation roadmaps.
• Collaborate with engineering, product, and business stakeholders to translate requirements into technical solutions.
• Provide technical guidance and architectural direction to engineering teams.

Skills

Software architecture

AI engineering

Platform engineering

Hands-on LLM

Agent orchestration

Multi-agent systems

Tools

LangChain

LangGraph

Semantic Kernel

AutoGen

CrewAI

Azure OpenAI

AWS Bedrock

Google Vertex AI

Job description

Job Description Include

Agentic AI Architecture & Platform Engineering :

• Design and architect scalable, enterprise-grade agentic AI platforms and solutions.
• Define agent orchestration patterns, runtime architecture, workflows, and multi-agent communication mechanisms.
• Architect prompt engineering frameworks, tool calling, connectors, and model integration strategies.
• Design memory management, state persistence, and context management mechanisms.
• Evaluate and implement RAG architectures and vector database integrations wherever applicable.

LLM & AI Integration

• Design and integrate LLM-based applications using leading commercial and open-source models.
• Establish model integration patterns, routing, fallback mechanisms, and model selection strategies.
• Define reusable components and frameworks for agent development and deployment.
• Optimize model performance, latency, scalability, and token consumption.

AI Evaluation, Monitoring & Observability

• Define evaluation frameworks to measure agent accuracy, reliability, consistency, and performance.
• Establish mechanisms for testing prompts, tools, workflows, and end-to-end agent behaviour.
• Implement telemetry, monitoring, tracing, and observability for agentic systems.
• Define mechanisms to identify hallucinations, failures, and performance bottlenecks.

AI Safety, Security & Governance

• Identify and address security and governance gaps in agentic AI architectures.
• Establish guardrails for responsible AI implementation, including access controls and data protection.
• Define policies for tool permissions, human-in-the-loop controls, and agent execution boundaries.
• Ensure appropriate compliance, auditability, and governance standards are incorporated into the architecture.

Technical Leadership & Architecture Assessment

• Assess existing AI and agentic platform architectures and map technical dependencies.
• Identify architecture gaps, integration challenges, scalability risks, and opportunities for improvement.
• Recommend target-state architecture and implementation roadmaps.
• Collaborate with engineering, product, and business stakeholders to translate requirements into technical solutions.
• Provide technical guidance and architectural direction to engineering teams.

Primary Skills

• 5+ years of experience in software architecture, AI engineering, platform engineering, or related technical roles, including recent hands-on experience building LLM-powered and agentic AI systems.
• Strong understanding of agent orchestration frameworks, agent runtimes, multi-agent systems, and workflow automation.
• Hands-on experience with LLM integrations, prompt engineering, function calling, and tool orchestration.
• Experience designing memory, state management, context handling, and RAG-based architectures.
• Knowledge of vector databases, embeddings, semantic search, and retrieval mechanisms.
• Experience implementing AI guardrails, evaluation frameworks, and observability solutions.
• Strong understanding of AI security, governance, data privacy, and responsible AI principles.
• Ability to assess complex architectures, identify dependencies, and recommend scalable technical solutions.
• Strong communication and stakeholder management skills.

Secondary Skills

• Experience with frameworks and platforms such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent.
• Exposure to cloud-based AI ecosystems such as Azure OpenAI, AWS Bedrock, or Google Vertex AI.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, or Azure AI Search.
• Familiarity with agent evaluation tools, tracing, and monitoring frameworks.
• Experience designing enterprise AI platforms with security, scalability, and cost optimization considerations.

(ref:hirist.tech)