AI/ML Architect

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Job Title: Technical Architect – Enterprise Applications & AI/ML Solutions

Job Summary

Technical Architect

Python

C# .Net

Retrieval-Augmented Generation (RAG) systems

Key Responsibilities

• Lead the architectural design and development of enterprise-level applications and AI/ML solutions.
• Collaborate with business and technical stakeholders to translate requirements into scalable and maintainable architectures.
• Design and implement end-to-end solutions with a focus on performance, security, and maintainability.
• Provide technical leadership and mentoring to development teams.
• Conduct code reviews, enforce best practices, and ensure adherence to architectural standards.
• Participate in technical discussions with client teams, providing guidance and strategic recommendations.
• Oversee integration of AI/ML components, with a strong emphasis on RAG-based solutions.
• Evaluate emerging technologies and drive innovation in architecture and solutioning.
• Work closely with DevOps and QA teams to support CI/CD, automated testing, and deployment practices.
  

Required Skills And Qualifications

• 8–10 years of overall experience in software development and architecture.
• Proven experience designing and building large-scale enterprise applications.
• Proficient in either Python or C# .Net, with strong coding and debugging skills.
• Solid understanding of architectural patterns (e.g., microservices, event-driven, layered architecture).
• Hands-on experience with cloud platforms (e.g., AWS, Azure, or GCP).
• Strong experience working with databases (SQL and NoSQL), APIs, and integration patterns.
• Exposure to AI/ML solutions, especially RAG-based architectures (e.g., combining LLMs with vector databases, context-aware search).
• Familiarity with vector databases like FAISS, Pinecone, or Weaviate.
• Strong understanding of LLMs, embeddings, prompt engineering, and data pipelines.
• Excellent communication and interpersonal skills.
• Experience interacting with client stakeholders in technical discussions.
• Ability to manage technical teams, assign tasks, and ensure high-quality deliverables.
  

Preferred Qualifications

• Experience with containerization (Docker, Kubernetes).
• Exposure to MLOps and deployment of AI models in production.
• Experience in Agile/Scrum methodologies.
• Certifications in cloud architecture (AWS Solutions Architect, Azure Architect, etc.) or AI/ML will be a plus.