Creative AI Engineer

This role is for one of the Weekday's clients

Salary range: Rs 500000 - Rs 1600000 (ie INR 5 - 16 LPA)

Min Experience: 4 years

Location: Pune, Mumbai, Gurugram, Bengaluru, Chennai, Hyderabad

JobType: full-time

Requirements

Key Responsibilities

• Multi-Modal AI Pipeline: Develop and optimize AI processes for analyzing static images, media assets, video and motion content, and documents by leveraging third-party AI services, APIs, and multi-modal foundation models from major cloud hyper-scalers.
• LLM Integration & Orchestration: Design, implement, and maintain an AI provider abstraction layer that supports all leading cloud AI hyper-scalers, including Google Gemini and Azure OpenAI, with intelligent routing based on task complexity and cost efficiency.
• Prompt Engineering: Create and refine channel-specific prompts tailored for advertising and creative effectiveness analysis across platforms such as Facebook, Instagram, and YouTube; embed domain expertise into system prompts; and optimize prompts for structured JSON outputs from LLMs.
• RAG Implementation: Develop and sustain retrieval-augmented generation workflows using pgvector, implement hybrid search combining full-text and vector similarity, and design embeddings to enhance media asset processing.
• Custom AI Development: Improve our proprietary optimization model and develop algorithms that integrate third-party metrics into dynamic strength scores.
• AI Agent Development: Build and oversee conversational AI agents capable of tool-calling, manage state for multi-turn dialogues, and create autonomous decision-making systems to support creative workflows.
• AI-powered Asset Optimization: Oversee and advance our AI-driven solution for creative asset optimization, using creative performance analysis to automate enhancements and implement incremental edits to improve creative assets.
• Performance Optimization: Minimize inference latency, implement smart caching for embeddings, optimize token usage across providers, and develop cost-effective strategies for selecting AI models.

Required Skills

• AI/Gen-AI Foundations: Minimum of 4 years' experience working with LLMs (including Gemini models, GPT, Claude) and a deep understanding of embeddings and vector search techniques.
• Multi-Modal AI: Demonstrated experience processing creative assets such as images and video using AI models, knowledge of computer vision fundamentals, expertise in multi-modal creative generation, and proficiency in prompt engineering for media understanding and generation.
• Scaled AI Deployment: Proven experience deploying AI solutions in production, managing asynchronous processing, implementing retry mechanisms, and handling errors in AI APIs.
• Python + AI Frameworks: Strong proficiency in Python, experience with cloud AI SDKs including Google Gemini, Vertex, Azure OpenAI, and familiarity with AI orchestration frameworks.
• RAG Systems: Practical experience building retrieval-augmented generation solutions, utilizing vector databases, and implementing semantic search.
• Creative AI Workflows: Hands-on expertise in building, managing, and evolving creative workflows powered by AI, including creative asset adaptation, automation, analysis, or production.

Highly Valued

• Experience in creative AI, creative technology, media, or advertising technology sectors.
• Previous work with various creative AI tools and frameworks.
• Advanced skills in prompt optimization and structured output formatting.
• Experience fine-tuning foundation models.
• Knowledge of AI safety practices, including content filtering, audit logging, and watermarking.
• Understanding of advertising and media performance metrics such as engagement, recall, attention, and memorability.

AI Stack You'll Work With

• AI Models: Proprietary AI APIs for creative analysis, vision models, multi-modal AI models, LLMs including Google Gemini, Azure OpenAI, and Anthropic Claude.
• Internal AI: Proprietary optimization models, custom dynamic strength scoring algorithms, and channel-specific prompt libraries.
• Infrastructure: PostgreSQL with pgvector (HNSW indexes), asynchronous processing pipelines, AI observability frameworks, and Model Context Protocol (MCP) for tool integration.
• Frameworks: Proprietary Django-based Python framework for AI workflows and orchestration.

What You'll Build

• An AI pipeline that analyzes creative assets for cognitive demand, engagement, memorability, and attention effectiveness.
• A generative AI system that creates optimized creative variants based on recommendations.
• A vector similarity engine that identifies conceptually similar creatives from millions of assets.
• Multi-modal agents that reason across images, video, and text to deliver actionable insights.
• A cost optimization layer that routes requests to the most appropriate AI models.
• A compliance system (planned) for validating creatives against advertising standards.

Must-have Skills

Generative AI, Azure OpenAI, Python

Good-to-have Skills

Creative AI Engineer, Generative AI Engineer, LLM Engineer