Senior AI/ML Engineer Job ID: 397570

Kuku FM, Inc.; Mumbai; On-site; INR 3,600,000 - 6,000,000; Full time

Job summary

Kuku FM, Inc. is seeking a Senior AI/ML Engineer to build the next-generation AI-powered content platform. You will work on LLMs, instruction tuning, multimodal AI, and AI-generated microdramas, transforming ideas into screenplays and production-ready video prompts.

You will own ML systems end-to-end, from training and evaluation to deployment, collaborating with content, product, and engineering teams to push the boundaries of AI-driven storytelling.

Qualifications

• Bachelor's or Master's in Computer Science, ML, Statistics, or related field; 4–6 years of relevant experience.
• Hands-on experience training or fine-tuning large language models using PyTorch, Hugging Face Transformers, PEFT/LoRA, or equivalent tech.
• Production experience with end-to-end ML systems, data pipelines, model serving, and monitoring.
• Strong understanding of LLMs, transformers, embeddings, RAG, instruction tuning, evaluation, and agentic AI systems.
• Experience with LangGraph, LangChain, or similar agentic orchestration frameworks is a plus.

Responsibilities

• Design and build LLM-powered systems for screenplay generation, dialogue, and content transformation.
• Develop multimodal AI workflows for video generation and content understanding.
• Own end-to-end ML pipelines from training to deployment and monitoring.

Skills

LLM training

GenAI systems

Python engineering

ML evaluation

Multimodal AI

Education

Bachelor's/Master's in CS/ML

Tools

PyTorch

Transformers

PEFT/LoRA

Job description

About the job

As a Senior AI/ML Engineer, you will be instrumental in building Kuku's next-generation AI-powered content creation platform, with a focus on LLMs, instruction tuning, multimodal AI, and AI-generated microdramas. You will work on building systems that understand thousands of hours of existing microdrama content and transform creative ideas into engaging screenplays and production-ready video generation instructions.

This role will involve working across LLM training, content understanding, agentic workflows, screenplay generation, evaluation, and AI video generation, while owning key ML systems end-to-end. You will work closely with content, product, and engineering teams to build AI systems that directly impact how stories are created at Kuku.

Responsibilities

• LLM & Instruction Tuning: Design and build large-scale instruction-tuning datasets from Kuku's microdrama corpus and fine-tune LLMs for screenplay generation, dialogue, story continuation, rewriting, and content transformation.

• Microdrama Intelligence: Build ML/LLM systems to understand transcripts and extract characters, relationships, scenes, story arcs, dramatic beats, hooks, conflicts, reveals, twists, and cliffhangers.

• AI Screenwriting: Develop LLM-powered systems that transform story ideas into episode arcs, screenplays, scene breakdowns, dialogue, and structured production-ready scripts.

• Multimodal AI & Video Generation: Build systems leveraging LLMs, VLMs, audio models, image generation, and video generation models to automate the content creation lifecycle.

• AI Video Pipeline: Design the intelligence layer that converts screenplays into structured, production-ready prompts and instructions for video generation models such as Seedance.

• Agentic Systems & GenAI: Design and implement advanced agentic workflows using frameworks such as LangGraph to orchestrate story planning, screenplay generation, critique, rewriting, continuity validation, and video prompt generation.

• Story & Character Consistency: Build systems to maintain consistency of characters, relationships, locations, timelines, and story states across multi-episode microdrama series.

• Evaluation & Optimization: Design evaluation frameworks for measuring screenplay quality, narrative coherence, character consistency, hook strength, cliffhanger quality, and AI-video generatability.

• Technical Ownership: Provide technical leadership in ML model formulation, experimentation, architecture, and deployment. Take end-to-end ownership of critical AI systems and guide other ML engineers.

• Architecture & Strategy: Define the technical architecture and roadmap for Kuku's AI content generation platform, evaluating foundation models, fine-tuning approaches, RAG, preference optimization, and agentic architectures.

• MLOps & Infrastructure: Design and maintain scalable, reliable, and cost-efficient training, inference, data, and evaluation pipelines for LLM and multimodal AI systems.

Preferred Qualifications

• Education & Experience: Bachelor's or Master's in Computer Science, Machine Learning, Statistics, or a related engineering field, with 4-6 years of relevant experience, including experience owning and delivering complex ML/AI systems.

• LLM & Model Training: Hands-on experience training or fine-tuning large language models using frameworks such as PyTorch, Hugging Face Transformers, PEFT/LoRA, or equivalent technologies.

• Production Experience: Proven track record of productionising ML/GenAI systems, including designing and managing end-to-end ML systems, data pipelines, model serving, and monitoring.

• Generative AI Expertise: Strong understanding of LLMs, transformers, embeddings, RAG, instruction tuning, evaluation, and agentic AI systems.

• Agentic Systems: Experience building LLM orchestration frameworks using tools such as LangGraph, LangChain, or equivalent technologies.

• Multimodal AI: Experience working with VLMs, speech/audio models, image generation, video understanding, or video generation models is highly desirable.

• ML Evaluation: Experience designing evaluation frameworks, human preference datasets, automated evaluation, or model-based evaluation for ML/GenAI systems.

• Strong ML Fundamentals: Strong understanding of machine learning, deep learning, NLP, transformers, embeddings, and model optimization.

• Engineering: Strong Python programming and software engineering fundamentals with experience building scalable ML services and pipelines.

• Data & Infrastructure: Experience with distributed data processing, vector databases, cloud platforms, GPUs, or MLOps infrastructure.

• Research Awareness: Stay up-to-date with the latest advancements in LLMs, multimodal AI, generative AI, AI agents, and applied machine learning.