Generative AI Systems Engineer – Vision-Language Models

Job Description Role Summary We are seeking a Generative AI Systems Engineer to design, evaluate, and optimize Vision-Language Model (VLM) systems for real-world applications. This role requires a combination of: Model understanding Experimental rigor Systems and production thinking You will work on benchmarking, fine-tuning, and deploying multimodal models , with a strong emphasis on tradeoff analysis across accuracy, latency, and cost . Key Responsibilities Model Evaluation & Benchmarking Evaluate pretrained VLMs on domain-specific datasets Define and justify appropriate evaluation metrics Analyze model behavior, including systematic failure modes Model Adaptation & Fine-Tuning Implement parameter-efficient fine-tuning techniques (e.g., LoRA, QLoRA ) Optimize training under limited data and compute constraints Make data-centric and model-centric improvements with clear justification Experimental Rigor Design controlled experiments to compare baseline vs improved models Quantify improvements across: accuracy latency cost Provide clear, defensible explanations for observed outcomes System Design & Deployment Architect scalable inference pipelines for multimodal models Optimize for: low latency high throughput cost efficiency Implement serving layers (API/service) with reproducible environments Data Engineering Build pipelines to process and align: images textual queries structured metadata Analyze dataset characteristics, including biases and distribution gaps Qualifications B.E/B. Tech Additional Information 5â€7 years of industry experience in ML/AI systems Strong proficiency in Python and ML frameworks (e.g., PyTorch) Experience with VLMs , LLMs or any other multimodal models Understanding of model evaluation and experimentation practices Familiarity with ML system design (inference, scaling, optimization) Preferred Qualifications Experience with Vision-Language Models (e.g., LLaVA, BLIP, Flamingo-style architectures) Hands-on experience with parameter-efficient fine-tuning methods Knowledge of model optimization techniques : quantization batching caching (e.g., embedding reuse) Experience with Docker / containerized deployments Exposure to large-scale or real-world datasets