About Asiacruit At Asiacruit, we connect top talent
with forward-thinking organizations across industries. Our mission
is to help businesses grow through smart, strategic, and
people-focused solutions. We support companies by providing
high-quality talent for local and global markets. Position Overview
We are looking for a Multimodal AI Engineer to develop and adapt
Vision-Language-Action (VLA) foundation models for autonomous
robotic systems. You will work at the intersection of multimodal
perception, foundation models, and robotic learning, helping AI
systems interpret visual inputs, plan complex tasks, and translate
instructions into physical actions. This role focuses on
fine-tuning multimodal models, developing multi-camera perception
pipelines, implementing action-generation architectures, and
optimizing models for deployment on edge AI platforms. You will
collaborate with AI researchers and robotics engineers to turn
research concepts into practical, high-performance AI capabilities.
Key Responsibilities Develop, fine-tune, and adapt Vision
Transformers (ViTs) and multimodal foundation models for robotic
perception and action generation. Implement action-chunking
architectures, including Action Chunking with Transformers (ACT)
and Diffusion Policy, to support long-horizon robotic task
planning. Build multi-camera fusion pipelines that combine visual
inputs into coherent representations for VLA perception and
decision-making. Develop and optimize multimodal feature extraction
pipelines that integrate visual perception with downstream robotic
planning and action-generation systems. Integrate computer vision
capabilities,



including face recognition and perimeter threat
assessment, into multimodal AI workflows. Apply parameter-efficient
fine-tuning (PEFT) techniques, including LoRA, to adapt foundation
models for specific robotic applications. Work with Transformer
architectures, visual embeddings, and vector search techniques to
improve multimodal representation and information retrieval.
Profile and optimize multimodal models through pruning and other
model compression techniques to improve inference efficiency.
Prepare and optimize model weights for downstream deployment using
NVIDIA TensorRT and related inference optimization tools. Evaluate
model performance through systematic experiments, benchmarking, and
iterative improvements. Collaborate with researchers and
engineering teams across VLA, robotics controls, and edge AI to
support end-to-end system development. Qualifications Master's
degree or Ph.D. in Computer Science, Artificial Intelligence,
Robotics, Machine Learning, Computer Vision, or a related field.
Approximately 3–5 years of relevant experience in multimodal AI,
deep learning, computer vision, foundation models, or robotic
learning. Strong programming skills in Python and hands-on
experience with PyTorch. Experience developing, training, or




fine-tuning Transformer-based architectures, Vision Transformers,
or multimodal deep learning models. Understanding of
vision-language models, multimodal representation learning, and the
integration of visual information into AI decision-making systems.
Practical experience with parameter-productive fine-tuning methods,
such as LoRA or other PEFT techniques. Familiarity with robotic
learning and action-generation approaches, including ACT, Diffusion
Policy, imitation learning, or reinforcement learning. Experience
with multi-camera perception, visual feature fusion, or related
computer vision pipelines. Understanding of model profiling,
pruning, quantization, or other model optimization techniques.
Familiarity with NVIDIA DeepStream, TensorRT, or GPU-accelerated
inference is an advantage. Knowledge of embeddings, vector search,
and multimodal data processing is a plus. Strong analytical and
problem-solving skills, with the ability to conduct experiments and
translate research findings into practical engineering solutions.
Why Join? Work on emerging technologies at the intersection of
multimodal AI, foundation models, and robotics. Develop AI systems
that connect visual perception, intelligent planning, and physical
execution. Explore advanced approaches to VLA model adaptation,
action generation, and multimodal learning. Contribute to
real-world AI applications involving autonomous systems and
intelligent perception. Collaborate with researchers and engineers
working on challenging AI research and development
projects.

📌 Multimodal AI Engineer (Robotics & Foundation Models) (New Delhi)
🏢 Asiacruit BPO
📍 New Delhi