Solution Architect

Role Summary:

The Solution Architect is responsible for designing robust, scalable, and high-performance AI and data-driven systems that align with enterprise goals. This role serves as a critical technical leaderbridging AI/ML, data engineering, ETL, cloud architecture, and application development. The ideal candidate will have deep experience across traditional and generative AI, including Retrieval-Augmented Generation (RAG) and agentic AI systems, along with strong fundamentals in data science, modern cloud platforms, and full-stack integration.

Key Responsibilities:

• Design and own the end-to-end architecture of intelligent systems including data ingestion (ETL/ELT), transformation, storage, modeling, inferencing, and reporting.
• Architect GenAI-powered applications using LLMs, vector databases, and RAG pipelines; Agentic Workflow, integrate with enterprise knowledge graphs and document repositories.
• Lead the design and deployment of agentic AI systems that can plan, reason, and interact autonomously within business workflows.
• Collaborate with cross-functional teams including data scientists, data engineers, MLOps, and frontend/backend developers to deliver scalable and maintainable solutions.
• Define patterns and best practices for traditional ML and GenAI projects, covering model governance, explainability, reusability, and lifecycle management.
• Ensure seamless integration of ML/AI systems via RESTful APIs with frontend interfaces (e.g., dashboards, portals) and backend systems (e.g., CRMs, ERPs).
• Architect multi-cloud or hybrid cloud AI solutions, leveraging services from AWS, Azure, or GCP for scalable compute, storage, orchestration, and deployment.
• Provide technical oversight for data pipelines (batch and real-time), data lakes, and ETL frameworks ensuring secure and governed data movement.
• Conduct architecture reviews, mentor engineering teams, and drive design standards for AI/ML, data engineering, and software integration.

Qualifications:

• Bachelors or Master’s degree in Computer Science, Engineering, or a related field.
• 10+ years of experience in software architecture, including at least 4 years in AI/ML-focused roles.

Required Skills:

• Expertise in machine learning (regression, classification, clustering), deep learning (CNNs, RNNs, transformers), and NLP.
• Experience with Generative AI frameworks and services (e.g., OpenAI, LangChain, Azure OpenAI, Amazon Bedrock).
• Strong hands-on Python skills, with experience in libraries such as Scikit-learn, Pandas, NumPy, TensorFlow, or PyTorch.
• Proficiency in RESTful API development and integration with frontend components (React, Angular, or similar is a plus).
• Deep experience in ETL/ELT processes using tools like Apache Airflow, Azure Data Factory, or AWS Glue.
• Strong knowledge of cloud-native architecture and AI/ML services on either one of the cloud AWS, Azure, or GCP.
• Experience with vector databases (e.g., Pinecone, FAISS, Weaviate) and semantic search patterns.
• Experience in deploying and managing ML models with MLOps frameworks (MLflow, Kubeflow).
• Understanding of microservices architecture, API gateways, and container orchestration (Docker, Kubernetes).
• Having forntend exp is good to have.