Solution Architect

Company: C5i

Website: Visit Website

Business Type: Consulting Firm

Company Type: Product & Service

Business Model: B2B

Funding Stage: Pre-seed

Industry: Analytics

Job Description

We’re seeking an experienced Solution Architect with strong exposure to AI, Machine Learning (ML), and Generative AI technologies. The ideal candidate combines deep technical understanding with the ability to design, integrate, and scale AI-driven solutions within enterprise environments — leveraging orchestration, automation, and intelligent agent frameworks.

Key Responsibilities

• Design and own full-stack solution architectures (frontend, backend, APIs, databases, cloud) for enterprise applications.
• Define and enforce architectural standards, coding practices, and integration patterns.
• Lead technical design sessions and guide development teams through implementation.
• Architect and integrate agentic systems (e.g., autonomous agents, multi-agent orchestration) into full-stack applications.
• Collaborate with AI/ML teams to embed intelligent features using LLMs, RAG pipelines, and cognitive services.
• Evaluate and implement frameworks like LangChain, Semantic Kernel, or AutoGPT for agentic workflows.
• Provide technical mentorship to developers and engineers across the stack.
• Conduct code and architecture reviews to ensure performance, scalability, and security.
• Stay ahead of emerging trends in AI, agentic systems, and full-stack development.
• Present architectural decisions and trade-offs to leadership and clients.
• Collaborate with business and technical stakeholders to identify AI use cases and translate them into scalable architectures.
• Evaluate and select appropriate AI tools, frameworks, and cloud services (e.g., Azure OpenAI, AWS Bedrock, Vertex AI, Hugging Face, LangChain, CrewAI, n8n).
• Implement AI orchestration and agent-based workflows using tools such as CrewAI, LangChain, Model Context Protocol (MCP), or custom microservice architectures.
• Define and oversee data and model pipelines, including governance, MLOps, and observability.
• Work closely with product managers, business analysts, and UX designers to align technical solutions with business goals.
• Partner with data engineers, ML engineers, and developers to build production-grade AI APIs, agents, and automation workflows.
• Integrate AI capabilities into enterprise systems (CRM, ERP, data platforms, and custom applications).
• Ensure compliance with ethical AI, responsible AI, and data privacy standards.
• Develop architectural blueprints, PoCs, and reference implementations to accelerate adoption.
  
  

• Bachelor’s or master’s degree in computer science, Engineering, or related field.
• 6–7 years of experience in full-stack development and solution architecture.
• 2+ years of experience with AI and agentic technologies:
• LLMs (OpenAI, Azure OpenAI, etc.)
• Agentic frameworks (LangChain, Semantic Kernel, etc.)
• ML model integration and orchestration
• Proficiency in modern full-stack technologies:
• Frontend: React, Angular, or Vue.js
• Backend: Node.js, .NET Core, Python (FastAPI, Flask)
• Databases: SQL, NoSQL (MongoDB, Cosmos DB)
• Cloud: Azure, AWS, or GCP
• Experience with microservices, RESTful APIs, and event-driven architectures.
• Knowledge of DevOps practices and CI/CD pipelines.
• Strong understanding of LLM architecture, prompt engineering, RAG (Retrieval-Augmented Generation), and vector databases (e.g., Pinecone, FAISS, Chroma, Weaviate).
• Familiarity with AI orchestration frameworks and workflow automation tools (e.g., LangChain, CrewAI, n8n, MCP).
• Solid grounding in data architecture, API design, and cloud platforms (AWS, Azure, GCP).
• Knowledge of ML frameworks (TensorFlow, PyTorch, Scikit-learn) and GenAI SDKs (OpenAI, Anthropic, LlamaIndex).
• Demonstrated ability to design and integrate AI components within enterprise applications and cloud-native architectures.
• Strong communication skills, capable of engaging both technical and non-technical stakeholders.
  
  

• Experience with MLOps / AIOps platforms (MLflow, Kubeflow, SageMaker, Vertex AI Pipelines).
• Exposure to multi-modal AI (text, image, speech).
• Certifications in cloud architecture or AI/ML (Azure AI Engineer, AWS ML Specialty, GCP AI Engineer).
• Contributions to AI research, open-source projects, or technical communities.