Solutions Architect

Were 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.

Required Skills & Qualifications:

• Bachelors or masters degree in computer science, Engineering, or related field.
• 67 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.

Preferred Qualifications/Nice to have:

• 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 .