ProductNOVA
Website:
productnova.in
Job details:
Role Overview:
We are seeking an experienced AI / ML Developer with strong hands-on expertise in large language models (LLMs) and AI-driven application development. The ideal candidate will have practical experience working with GPT and Anthropic models, building and training B2B products powered by AI, and leveraging AI-assisted development tools to deliver scalable and intelligent solutions.
Key Responsibilities :
Model Analysis & Optimization :
Analyze, customize, and optimize GPT and Anthropic-based models to ensure reliability, scalability, and performance for real-world business use cases.
AI Product Design & Development :
Design and build AI-powered products, including model training, fine-tuning, evaluation, and performance optimization across development lifecycles.
Prompt Engineering & Response Quality :
Develop and refine prompt engineering strategies to improve model accuracy, consistency, relevance, and contextual understanding.
AI Service Integration :
Build, integrate, and deploy AI services into applications using modern development practices, APIs, and scalable architectures.
AI-Assisted Development Productivity :
Leverage AI-enabled coding tools such as Cursor and GitHub Copilot to accelerate development, improve code quality, and enhance efficiency.
Cross-Functional Collaboration :
Work closely with product, business, and engineering teams to translate business requirements into effective AI-driven solutions.
Model Monitoring & Continuous Improvement :
Monitor model performance, analyze outputs, and iteratively improve accuracy, safety, and overall system effectiveness.
Qualifications :
Hands-on experience analyzing, developing, fine-tuning, and optimizing GPT and Anthropic-based large language models.
Strong expertise in prompt design, experimentation, and optimization to enhance response accuracy and reliability.
Proven experience building, training, and deploying chatbots or conversational AI systems.
Practical experience using AI-assisted coding tools such as Cursor or GitHub Copilot in production environments.
Solid programming experience in Python or similar languages, with strong problem-solving and development fundamentals.
Experience working with embeddings, similarity search, and vector databases for retrieval-augmented generation (RAG).
Knowledge of MLOps practices, including model deployment, versioning, monitoring, and lifecycle management.
Exposure to cloud environments such as AWS or Azure for deploying and managing AI solutions.
Familiarity with APIs, microservices architecture, and system integration for scalable AI applications.
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