Multi Modal AI Senior Developer

We are seeking a seasoned and experienced Multi Modal AI Senior Developer with a deep expertise in leveraging Generative AI for creative and content generation at scale.
The ideal candidate would have a deep understanding of Multi Modal AI, the ability to leverage Gen-AI models for all creative output and content types, and the ability to work across all content and interaction modalities – including text, visuals, audio/speech and video. A strong foundation in Large Language models and Vision Language Models (VLM) is also highly desirable.
As a Multi Modal AI Senior Developer, you will play a key role in building cutting-edge products and innovative solutions, combining the full power of Creative AI workflows, Generative AI, LLMs, and Agentic AI.
Your primary focus will be on building bespoke products and creative workflows leveraging Gen-AI models to help build out our creative product portfolio for some of our largest, most strategic enterprise product solutions.
The candidate should have a good technical background in Product Development, Cloud-Native App Dev, Front-End and Back-End Web Application Development, and the ability to build these solutions in cloud environments such as Azure and AWS, integrating with the appropriate multi modal AI services.
The candidate will also need to have strong expertise with Cloud AI services such as Azure OpenAI, AWS Bedrock, and Google Gemini, and all the foundation models hosted within those services. Additionally, hands-on experience with a variety of foundation models, Vision Language Models (VLMs), Creative AI Services and APIs, and the ability to seamlessly integrate all of these together, in an automated workflow using APIs and AI Assistants, will be an essential skillset.

Job Description:

Key Skills required :

Generative AI, Multi Modal AI

Creative AI solutions and workflows across all creative content types including Copy/Text, Imagery, Key Visuals, Characters, Avatars, Audio, Speech and Video AI

Creative AI Automation workflows with content creation and content editing at scale, using AI services and AI APIs

Experience with multiple Multi Modal AI Foundation Models

LLM, LLM App Dev

AI Agents, Agentic AI Workflows

Responsibilities :

Design and build web apps and solutions that leverage Creative AI Services, Multi Modal AI models, and Generative AI workflows Leverage Multi modal AI capabilities supporting all content types and modalities, including text, imagery, audio, speech and video Build creative automation workflows that help produce creative concepts, creative production deliverables, and integrated creative outputs, leveraging AI and Gen-AI models Integrate AI Image Gen Models and AI Image Editing models from key technology partners Integrate Text / Copy Gen Models for key LLM providers Integrate Speech / Audio Gen and Editing models for use cases such as transcription, translation, and AI generated audio narration Integrate AI enabled Video Gen and Video Editing models Fine-Tune Multi Modal AI models for brand specific usage and branded content generation Constantly Research and explore emerging trends and techniques in the field of generative AI and LLMs to stay at the forefront of innovation. Drive product development and delivery within tight timelines Collaborate with full-stack developers, engineers, and quality engineers, to develop and integrate solutions into existing enterprise products. Collaborate with technology leaders and cross-functional teams to develop and validate client requirements and rapidly translate them into working solutions. Develop, implement and optimize scalable AI-enabled products Integrate Gen-AI and Multi Modal AI solutions into Cloud Platforms, Cloud Native Apps, and custom Web Apps Execute implementation across all layers of the application stack – including front-end, back-end, APIs, data and AI services Build enterprise products and full-stack applications on the MERN + Python stack, with a clear separation of concerns across layers

Skills and Competencies:

Deep Hands-on Experience in Multi modal AI models and tools. Hands-on Experience in API integration with AI services Multi Modal AI – competencies :Hands-on Experience with intelligent document processing and document indexing + document content extraction and querying, using multi modal AI ModelsHands-on Experience with using Multi modal AI models and solutions for Imagery and Visual Creative – including text-to-image, image-to-image, image composition, image variations, etc.Hands-on Experience with popular AI Image Composition and Editing models from providers such as Adobe Firefly, Getty Images, ShutterStock, Flux and Flux Pro, and Stable Diffusion, and the ability to integrate them programmatically over API calls and workflowsHands-on Experience with Computer Vision and Image Processing using Multi-modal AI – for use cases such as object detection, automated captioning, automated masking, and image segmentation – again all done programmatically over API calls and WorkflowsHands-on Experience with using Multi modal AI for Speech – including Text to Speech, Speech to Text, and use of Pre-built vs. Custom VoicesHands-on Experience with building Voice-enabled and Voice-activated experiences, using Speech AI and Voice AI solutionsHands-on Experience with AI Character and AI Avatar development, using a variety of different tools and platformsFine-Tuning Creative AI Content models for Custom Styles, Custom Characters, and Custom Brand specific imageryFine-Tuning Speech Models for Custom VoicesGood understanding of advanced fine-tuning techniques such as LoRAAbility to execute and run fine-tuning workflows, end-to-end, in particular for Image Gen and Image Editing modelsHands-on Experience with leveraging APIs to orchestrate across Multi Modal AI modelsHands-on Experience with building workflows that orchestrate across Multi Modal AI modelsGood Experience with using AI Assistants to drive natural language interactions and orchestration with Multi Modal AI modelsGood Experience with use of AI Agents and Agentic AI workflows to drive dynamic orchestration across Multi Modal AI services and models Programming Skills :Good Expertise in MERN stack (JavaScript) including client-side and server-side JavaScriptGood Expertise in Python based development, including Python App Dev for Multi Modal AI IntegrationWell-rounded in both programming languagesStrong experience in client-side JavaScript Apps and building Static Web Apps + Dynamic Web Apps both in JavaScriptHands-on Experience in front-end and back-end developmentMinimum 2+ years hands-on experience in working with Full-Stack MERN apps, using both client-side and server-side JavaScriptMinimum 2 years hands-on experience in Python developmentMinimum 2 years hands-on experience in working with LLMs and LLM models, using Python LLM Dev Skills :Solid Hands-on Experience with building end-to-end RAG pipelines and custom AI indexing solutions to ground LLMs and enhance LLM outputGood Experience with building AI and LLM enabled Workflows Hands-on Experience integrating LLMs with external tools such as Web Search Ability to leverage advanced concepts such as tool calling and function calling, with LLM modelsHands-on Experience with Conversational AI solutions and chat-driven experiencesExperience with multiple LLMs and models – primarily GPT-4o, GPT o1, and o3 mini, and preferably also Gemini, Claude Sonnet, etc.Experience and Expertise in Cloud Gen-AI platforms, services, and APIs, primarily Azure OpenAI, and perferably also AWS Bedrock , and/or GCP Vertex AI .Hands-on Experience with Assistants and the use of Assistants in orchestrating with LLMsHands-on Experience working with AI Agents and Agent Services .

Nice-to-Have capabilities (Not essential) :

Hands-on Experience with building Agentic AI workflows that enable iterative improvement of output Hands-on experience with both Single-Agent and Multi-Agent Orchestration solutions and frameworks Hands-on experience with different Agent communication and chaining patterns Ability to leverage LLMs for Reasoning and Planning workflows, that enable higher order “goals” and automated orchestration across multiple apps and tools Ability to leverage Graph Databases and “Knowledge Graphs” as an alternate method / replacement of Vector Databases, for enabling more relevant semantic querying and outputs via LLM models. Good Background with Machine Learning solutions Good foundational understanding of Transformer Models Good foundational understanding of Diffusion Models Some Experience with custom ML model development and deployment is desirable. Proficiency in deep learning frameworks such as PyTorch, or Keras.  Experience with Cloud ML Platforms such as Azure ML Service, AWS Sage maker, and NVidia AI Foundry.

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