Description

Key Responsibilities

 

• Lead end-to-end product development, including product design, system architecture, and hands-on engineering of AI-driven platforms.
• Design and implement RAG (Retrieval-Augmented Generation) architectures, including data ingestion, embedding pipelines, vector databases, and LLM orchestration.
• Build and deploy scalable AI/ML solutions, including generative AI and LLM-based applications.
• Translate business requirements into technical product designs, including system workflows, user journeys, and architecture diagrams.
• Collaborate with product, design, engineering, and data science teams to deliver high-impact solutions.
• Develop APIs, microservices, and cloud-native applications supporting AI workloads.
• Own UI/UX design considerations, ensuring intuitive, efficient, and user-centric product experiences.
• Drive rapid prototyping, experimentation, and iteration of AI features.
• Ensure scalability, performance, security, and cost optimization across cloud environments.
• Implement MLOps practices, including model deployment, monitoring, evaluation, and continuous improvement.
• Establish feedback loops for AI systems to improve accuracy, relevance, and user satisfaction.
• Identify technical risks and lead mitigation strategies.
  
   

Required Qualifications

 

• 8+ years of software engineering experience, with 3+ years focused on AI/ML or data-driven applications.
• Strong hands-on experience designing and building RAG-based systems and LLM-powered applications.
• Experience with vector databases, embeddings, and semantic search frameworks.
• Proficiency in Python and experience with ML/AI frameworks (e.g., TensorFlow, PyTorch).
• Strong understanding of cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
• Experience with APIs, microservices, and distributed system design.
• Strong experience in product design thinking, including translating requirements into scalable technical and UX solutions.
• Familiarity with data pipelines, vector indexing, and retrieval optimization techniques.
  
   

Preferred Qualifications

 

• Experience building advanced RAG pipelines with tools such as LangChain, LlamaIndex, or similar frameworks.
• Familiarity with prompt engineering, fine-tuning, and LLM evaluation techniques.
• Knowledge of AI governance, responsible AI, and data privacy frameworks.
• Experience in SaaS or enterprise platform development.
• Proven track record of building 0-to-1 AI products.
• Exposure to frontend technologies with a strong UI/UX sensibility

Key Skills

Education

Any Gradute