AI Solutions Architect

RecruitBots AI; Delhi; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

RecruitBots AI in Delhi seeks an AI Solutions Architect with 5–8 years of experience to design scalable, secure AI-powered systems aligned with business needs. You will work with stakeholders, product managers, and engineering teams to translate complex requirements into production-ready architectures.

You'll explore Generative AI, LLM applications, RAG pipelines, and enterprise integrations on cloud platforms, define data flows and security controls, and guide teams from concept to

Qualifications

• Strong software engineering and system design fundamentals.
• Experience designing scalable enterprise integrations.
• Hands-on AI/LLM solution design experience.
• Knowledge of RAG, embeddings, vector databases.

Responsibilities

• Design end-to-end AI solution architectures aligned with business objectives.
• Translate use cases into technical designs and implementation plans.
• Evaluate AI models, LLM providers, frameworks, databases, and clouds.
• Design architectures for LLM apps, RAG systems, and AI agents.
• Define components, data flows, APIs, authentication and patterns.
• Establish standards for scalability, reliability, security and perf.
• Guide teams to production-ready implementations.
• Lead architecture reviews and PoC evaluations.
• Ensure responsible AI, data privacy, and governance.

Skills

Software engineering

System design

Enterprise integrations

AI/ML basics

Generative AI

LLM apps

RAG architectures

Embeddings

Vector databases

REST APIs

Microservices

Cloud platforms

Security & privacy

Stakeholder management

Technical presentation

Education

Bachelor's degree in CS/IT/Engineering

Master's degree or cloud certifications

Tools

LangGraph

LangChain

OpenAI

Anthropic Claude

Google Gemini

Pinecone

Weaviate

Qdrant

Docker

Kubernetes

Job description

About the role

We are looking for an AI Solutions Architect with 5–8 years of experience in software architecture, AI engineering, or enterprise technology solutions who can design scalable, secure, and reliable AI-powered systems aligned with business requirements.

You will work closely with business stakeholders, product managers, engineering teams, and clients to understand complex requirements, evaluate AI technologies, define end-to-end solution architectures, and guide the implementation of production-ready AI solutions.

The role provides exposure to Generative AI, LLM applications, AI agents, RAG architectures, enterprise AI integrations, cloud infrastructure, system design, and AI governance.

Responsibilities

• Design end-to-end AI solution architectures aligned with business objectives, technical requirements, and operational constraints.
• Translate business use cases into technical designs, implementation plans, and scalable AI solutions.
• Evaluate AI models, LLM providers, frameworks, databases, and cloud platforms to select appropriate technologies.
• Design architectures for LLM applications, RAG systems, AI agents, and multi-step automated workflows.
• Define system components, data flows, API integrations, authentication mechanisms, and communication patterns.
• Establish architectural standards for scalability, reliability, security, maintainability, and performance.
• Evaluate build-versus-buy decisions and recommend suitable AI platforms, third-party services, and development approaches.
• Design secure data processing and retrieval workflows that protect sensitive information.
• Define strategies for AI evaluation, observability, error handling, monitoring, and production support.
• Assess model performance, inference latency, infrastructure requirements, and operational costs.
• Guide engineering teams in translating architectural designs into production-ready implementations.
• Collaborate with stakeholders to gather requirements, explain technical trade-offs, and communicate solution recommendations.
• Lead technical discussions, architecture reviews, proof-of-concept evaluations, and solution demonstrations.
• Identify technical risks, integration challenges, and scalability limitations before deployment.
• Establish guidelines for responsible AI usage, access control, data privacy, and system governance.
• Stay updated with emerging AI technologies, enterprise architecture patterns, and AI deployment practices.

Required Skills

• Strong software engineering and system design fundamentals.
• Experience designing and implementing scalable software systems and enterprise integrations.
• Practical understanding of Artificial Intelligence, Machine Learning, Generative AI, and LLM-based applications.
• Knowledge of RAG architectures, embeddings, vector databases, and AI agent workflows.
• Strong understanding of REST APIs, microservices, databases, and distributed systems.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Understanding of authentication, authorization, data security, and privacy considerations.
• Ability to evaluate technology options and make architecture decisions based on business and technical constraints.
• Knowledge of system scalability, reliability, latency, and cost optimization.
• Experience documenting solution architectures, technical specifications, and design decisions.
• Strong stakeholder management, communication, and technical presentation skills.
• Ability to guide engineering teams and collaborate with product and business stakeholders.

Good to Have

• Hands-on experience designing enterprise-grade Generative AI or LLM-powered solutions.
• Experience with AI agent frameworks such as LangGraph, LangChain, or similar technologies.
• Familiarity with model providers such as OpenAI, Anthropic Claude, Google Gemini, or open-source LLMs.
• Experience designing RAG pipelines, hybrid search, reranking, and knowledge retrieval systems.
• Familiarity with vector databases such as Pinecone, Weaviate, Qdrant, or similar platforms.
• Experience with Docker, Kubernetes, CI/CD, and cloud-native application architectures.
• Knowledge of LLMOps, model evaluation, AI observability, and production monitoring.
• Experience with API gateways, event-driven architectures, and enterprise integration patterns.
• Familiarity with identity management, data governance, responsible AI, and security controls.
• Experience leading technical proof-of-concepts and moving successful AI experiments into production.
• Experience presenting technical solutions to clients, senior leadership, or business stakeholders.

Education

• Bachelor's degree in Computer Science, Information Technology, Engineering, Artificial Intelligence, or a related field.
• A master's degree or relevant cloud and architecture certifications are an advantage.
• Candidates with 5–8 years of relevant engineering, architecture, or AI solution development experience are encouraged to apply.

What We're Looking For

• Strong architectural thinking and the ability to connect business needs with practical technical solutions.
• A systems-level mindset focused on scalability, security, reliability, and long-term maintainability.
• Ability to evaluate emerging AI technologies without overengineering solutions.
• Confidence in explaining technical trade-offs to both technical and non-technical stakeholders.
• Strong ownership of solution design, technical decisions, and implementation outcomes.
• Ability to identify risks early and develop practical mitigation strategies.
• Leadership skills to guide engineering teams and coordinate cross-functional implementation.
• Interest in building enterprise-ready AI solutions that deliver measurable business value.
• Demonstrated experience designing and delivering production systems is highly valued.