Artificial Intelligence Engineer

Wizard Infoways Pvt. Ltd.

full-time

Required skills

Python
Airflow
asynchronous systems
CRM
data structures
end-to-end
Kafka
machine learning
NLP
SaaS
Pytorch

About the role

Wizard Infoways Pvt. Ltd.

Website: wizardinfoways.com
Job details:

^{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.}

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