Multimodal AI Engineer (Emerging Role)

Job Summary

We are seeking a highly skilled Multimodal AI Engineer to develop next-generation AI systems capable of understanding, processing, and generating content across multiple modalities, including text, images, audio, video, and structured data. The ideal candidate will work on cutting-edge foundation models, vision-language models (VLMs), and multimodal Generative AI applications that power enterprise and consumer experiences.

Job Description

As a Multimodal AI Engineer, you will design, train, fine-tune, and deploy advanced multimodal AI models that integrate computer vision, natural language processing, speech technologies, and generative AI. You will collaborate with research scientists, machine learning engineers, product teams, and cloud architects to build scalable AI solutions for real-world applications.

The role requires expertise in deep learning frameworks, multimodal model architectures, MLOps, cloud computing, and AI model optimization. You will help drive innovation in areas such as AI assistants, intelligent search, content generation, document understanding, video analytics, and autonomous systems.

Key Responsibilities

• Design and develop multimodal AI applications using text, image, audio, and video data.
• Train, fine-tune, and optimize Vision-Language Models (VLMs) and Large Language Models (LLMs).
• Build AI systems that combine natural language understanding with computer vision capabilities.
• Develop Retrieval-Augmented Generation (RAG) pipelines for multimodal content.
• Integrate AI models into enterprise products and cloud platforms.
• Create scalable data pipelines for training and inference workflows.
• Evaluate model performance, accuracy, robustness, and safety.
• Optimize AI models for latency, scalability, and production deployment.
• Collaborate with AI researchers to implement emerging architectures and techniques.
• Ensure responsible AI practices, fairness, security, and compliance standards.
• Monitor production AI systems and continuously improve model performance.
• Stay updated on the latest advancements in Generative AI and multimodal foundation models.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• 4+ years of experience in AI, Machine Learning, Deep Learning, or Data Science.
• Experience building and deploying machine learning models in production environments.
• Strong understanding of multimodal AI systems and foundation models.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities.

Required Technical Skills

Artificial Intelligence & Machine Learning

• Deep Learning
• Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Vision-Language Models (VLMs)
• Reinforcement Learning Fundamentals

Computer Vision

• Image Classification
• Object Detection
• Image Captioning
• Visual Question Answering (VQA)
• OCR and Document Intelligence

Natural Language Processing

• NLP
• Transformers
• Prompt Engineering
• Text Generation
• Semantic Search
• RAG Frameworks

Programming & Frameworks

• Python
• PyTorch
• TensorFlow
• Hugging Face Transformers
• OpenCV
• CUDA

Data & MLOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• Apache Spark
• Airflow

Cloud Platforms

• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)

Preferred Qualifications

• Experience with multimodal foundation models such as GPT-4o, LLaVA, CLIP, Gemini, or similar architectures.
• Knowledge of AI model compression and optimization techniques.
• Experience working with distributed AI training environments.
• Familiarity with vector databases and AI agent frameworks.
• Understanding of Responsible AI and AI governance principles.

Preferred Certifications

• AWS Certified Machine Learning Specialty
• Microsoft Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• NVIDIA Deep Learning Institute Certifications

Tools & Technologies

• Python
• PyTorch
• TensorFlow
• Hugging Face
• LangChain
• LlamaIndex
• OpenCV
• Docker
• Kubernetes
• MLflow
• Databricks
• Snowflake
• Pinecone
• Weaviate
• AWS
• Azure
• GCP

Success Metrics

• Successful deployment of multimodal AI applications.
• Improved model accuracy and user experience.
• Reduced inference latency and operational costs.
• Scalable AI infrastructure and deployment pipelines.
• Increased adoption of AI-powered products and services.
• Compliance with security, privacy, and responsible AI standards.