Overview

Lead AI architectural strategy and systems integration in a full-stack environment.

Responsibilities

• We are seeking an AI Architect with 6–9 years of overall IT experience, including 3–4 years of hands-on experience in Artificial Intelligence, Machine Learning, or Generative AI solutions. The ideal candidate will have a strong full stack engineering background combined with expertise in designing, developing, and deploying AI-powered systems at scale.
• This role involves defining AI architecture strategies, integrating AI capabilities into enterprise applications, enabling intelligent automation, and guiding engineering teams through the AI adoption journey.
• AI Solution Architecture & Strategy
• Design end-to-end AI solutions across data, model, application, and infrastructure layers.
• Translate business problems into AI-driven technical architectures.
• Define AI platform architecture, model deployment strategies, and integration patterns.
• Evaluate and select appropriate AI/ML frameworks, tools, and technologies.
• Provide architecture guidance for AI initiatives across the organization.
• AI/ML & Generative AI Development
• Design and implement machine learning and deep learning solutions where required.
• Architect and develop Generative AI applications including LLM integrations, Retrieval-Augmented Generation (RAG), prompt engineering frameworks, and AI agents.
• Work with frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, or similar tools.
• Develop intelligent features within enterprise applications.
• AI Integration with Enterprise Systems
• Integrate AI solutions with web applications, APIs, databases, and enterprise platforms.
• Design microservices and API-based architectures for AI services.
• Enable AI-powered automation across business workflows.
• Ensure seamless interaction between AI models and production systems.
• MLOps & Deployment
• Define model deployment strategies including real-time inference and batch processing.
• Implement MLOps pipelines for model training, versioning, monitoring, and retraining.
• Work with containerization and orchestration technologies such as Docker and Kubernetes.
• Implement CI/CD pipelines for AI/ML systems.
• Monitor model performance, drift, and reliability in production environments.
• Data Engineering & Infrastructure Collaboration
• Collaborate with data engineers to design data pipelines and feature engineering workflows.
• Define data requirements, storage strategies, and data governance considerations.
• Work with cloud platforms such as Azure, AWS, or GCP for AI infrastructure deployment.
• Optimize infrastructure for performance and cost efficiency.
• Performance, Security & Governance

Qualifications

• Strong full stack engineering background with experience in backend development (Python, Java, .NET, or Node.js), APIs, and microservices.
• Hands-on experience with AI/ML technologies including machine learning frameworks and Generative AI/LLM integrations.
• Strong understanding of AI system architecture and deployment patterns.
• Knowledge of data engineering and MLOps practices.
• Strong problem-solving and analytical skills.
• Exposure to vector databases (Pinecone, FAISS, Weaviate, etc.).
• Knowledge of NLP, computer vision, or recommendation systems.
• AI or cloud certifications are a plus.
• 6 to 9 years of overall IT experience.
• Minimum 3 to 4 years of hands-on experience in AI, Machine Learning, or Generative AI solutions.
• Architectural thinking and system design capability.
• Innovation and problem-solving mindset.
• Strong technical leadership and mentoring ability.
• Communication and stakeholder collaboration skills.
• Continuous learning orientation.
• Strong full stack engineering background with experience in backend development (Python, Java, .NET, or Node.js), APIs, and microservices.
• Hands-on experience with AI/ML technologies including machine learning frameworks and Generative AI/LLM integrations.
• Strong understanding of AI system architecture and deployment patterns.
• Knowledge of data engineering and MLOps practices.
• Strong problem-solving and analytical skills.
• Exposure to vector databases (Pinecone, FAISS, Weaviate, etc.).
• Knowledge of NLP, computer vision, or recommendation systems.
• AI or cloud certifications are a plus.
• 6 to 9 years of overall IT experience.
• Minimum 3 to 4 years of hands-on experience in AI, Machine Learning, or Generative AI solutions.
• Architectural thinking and system design capability.
• Innovation and problem-solving mindset.
• Strong technical leadership and mentoring ability.
• Communication and stakeholder collaboration skills.
• Continuous learning orientation.

Nice to have

• Experience with enterprise AI platforms
• AI or cloud certifications

Education

• Bachelor’s Degree in Engineering (Computer Science or Information Technology) is required.
• Candidates from other educational backgrounds may be considered if they have 100% relevant professional experience.
• Bachelor’s Degree in Engineering (Computer Science or Information Technology) is required.
• Candidates from other educational backgrounds may be considered if they have 100% relevant professional experience.

Benefits

• Technical leadership opportunities
• Innovative work culture