Artificial Intelligence Engineer

^{Company: Wizard Infoways Pvt. Ltd.}

^{Location: Noida (Onsite)}

^{Experience Required: 3–5 Years}

^{Job Overview}

^{Wizard Infoways Pvt. Ltd. is seeking an experienced and highly skilled AI Engineer to design and implement the AI foundation of our conversation intelligence SaaS product.}

^{The role focuses on building scalable AI systems that power insights from sales conversations, integrating call data, transcripts, CRM information, and user interactions into intelligent, context-aware systems.}

^{This is a product-focused, applied AI engineering role requiring strong execution capabilities, deep understanding of LLM systems, and the ability to work in a fast-paced early-stage environment.}

^{Key Responsibilities}

^{AI Architecture & System Design}

• ^{Design and develop the end-to-end AI architecture for a conversation analytics SaaS platform.}
• ^{Define how AI components interact with call recordings, transcripts, CRM data, and user behavior.}
• ^{Design integration between models, prompt systems, retrieval layers, and contextual data structures.}
• ^{Evaluate and decide between prompting, fine-tuning, API-based models, and self-hosted open-source models.}

^{Context Engineering & Retrieval Systems}

• ^{Build and maintain dynamic context layers over conversations and CRM workflows.}
• ^{Implement RAG-based retrieval systems for context-aware responses.}
• ^{Develop lightweight stateful memory systems for individual calls and leads.}
• ^{Design context graphs to represent lead stages, call outcomes, and follow-up actions.}

^{Model Integration & Evaluation}

• ^{Integrate and experiment with open-source and hosted LLMs for production use cases.}
• ^{Explore fine-tuning approaches where relevant to domain-specific requirements.}
• ^{Build evaluation frameworks for latency, cost, hallucination rate, and output quality.}
• ^{Continuously benchmark and optimize model performance in real-world scenarios.}

^{Production Engineering & Optimization}

• ^{Optimize inference pipelines for low-latency and cost-efficient production deployment.}
• ^{Ensure robustness of models on noisy, real-world sales conversation data.}
• ^{Design systems that scale efficiently at early-stage startup constraints.}

^{Product Collaboration}

• ^{Work closely with founders and product teams to translate business requirements into AI-driven features.}
• ^{Define success metrics for AI systems (e.g., conversion improvement, coaching quality, follow-up effectiveness).}
• ^{Contribute to product decisions with AI feasibility and system design insights.}

^{Early-Stage Execution Mindset}

• ^{Work in a fast-paced, pre-product-market-fit environment.}
• ^{Rapidly prototype, iterate, and run experiments (A/B or similar testing methods).}
• ^{Build practical, production-ready systems without over-engineering.}

^{Required Qualifications}

^Experience

• ^{3–5 years of total software engineering experience.}
• ^{2–3 years of hands-on experience in Machine Learning, NLP, or LLM-based systems.}

^{Technical Skills}

• ^{Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, Hugging Face).}
• ^{Experience with LLM applications, RAG pipelines, or conversational AI systems.}
• ^{Familiarity with integrating open-source models into production environments.}
• ^{Understanding of APIs, REST services, asynchronous systems, and production engineering principles (latency, monitoring, reliability).}

^Mindset

• ^{Product-focused approach with emphasis on user outcomes.}
• ^{Ability to work independently in a remote or early-stage startup environment.}
• ^{Strong problem-solving mindset with iterative improvement approach.}

^{Preferred Qualifications}

• ^{Experience in conversation intelligence, contact center analytics, or sales-focused SaaS products.}
• ^{Familiarity with data pipelines and MLOps tools (e.g., Airflow, Prefect, Kafka).}
• ^{Experience with cost-optimized deployments and infrastructure tuning for low-latency systems.}
• ^{Exposure to India-specific or cost-sensitive cloud deployment environments.}

^{What We Offer}

• ^{Opportunity to build core AI systems for a high-impact SaaS product.}
• ^{Direct collaboration with founders in shaping product direction.}
• ^{Fast-paced, innovation-driven startup environment.}
• ^{Ownership of critical AI architecture and product intelligence systems.}