Generative AI Systems Engineer – Vision-Language Models

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

B.E/B. Tech

• 5–7 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