Applied Machine Learning / Computer Vision Engineer
At a glance
Mid-level Machine Learning Engineering role at TechBiz Global GmbH. Sahibzada Ajit Singh Nagar · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build image and video pipelines for visual analysis, understanding, and real-time processing
- Combine vision, audio, and language inputs in multimodal systems, using detection, segmentation, classification, and tracking
- Adapt vision-language and transformer models, and add language models to products for reasoning and automated decisions
- Test, tune, and deploy models; improve their speed, accuracy, scale, and resource use in production
- Turn research methods into product features, build APIs, and monitor deployed models with engineering and product teams
What you bring
- At least one year in applied machine learning, computer vision, deep learning, or a related area
- Strong Python skills, practical PyTorch use, and working knowledge of OpenCV for image or video processing
- A solid grasp of deep learning, object detection or image segmentation, transformer designs, and vision-language models
- Experience evaluating and optimizing inference, moving models into production, and using Docker and Git
Who this fits
This mid-level role suits someone who can work independently on technical problems and also contribute with engineering and product teammates. You should enjoy applying research to practical AI products, rather than focusing only on experiments. The position is hybrid in Sahibzada Ajit Singh Nagar, India.
From the employer
We are currently seeking an Applied Machine Learning / Computer Vision Engineer to join one of our clients’ innovative AI teams.
This is an exciting opportunity to work on a next-generation AI platform combining Computer Vision, Multimodal AI, Applied Machine Learning, and Large Language Models.
Key Responsibilities
As an Applied ML / Computer Vision Engineer, you will:
Design, develop, and improve Computer Vision and image-processing pipelines.
Build solutions for video analysis, video understanding, and real-time visual processing.
Develop multimodal AI systems combining vision, audio, and language data.
Work with object detection, image segmentation, classification, tracking, and related Computer Vision techniques.
Implement and fine-tune Vision-Language Models and Transformer-based architectures.
Integrate Large Language Models into AI products for reasoning, analysis, and automated decision-making.
Evaluate, optimize, and deploy machine-learning models in production environments.
Improve model inference speed, accuracy, scalability, and resource efficiency.
Translate research papers and emerging AI techniques into practical product features.
Build APIs and production-grade services for AI model integration.
Collaborate with engineering and product teams to understand business requirements and deliver effective AI solutions.
Monitor model performance and continuously improve deployed systems.
Maintain clear, reusable, and well-documented code.
1–4 years of experience in Applied Machine Learning, Computer Vision, Deep Learning, or a related field.
Strong programming skills in Python.
Hands-on experience with PyTorch.
Practical knowledge of OpenCV and image or video processing.
Strong understanding of Deep Learning fundamentals.
Experience with object detection and/or image segmentation models.
Knowledge of Transformer architectures.
Experience working with Vision-Language Models.
Familiarity with the Hugging Face ecosystem.
Experience with model inference, evaluation, and optimization.
Understanding of how to move models from experimentation into production.
Experience using Docker and Git.
Ability to independently investigate technical challenges and propose practical solutions.
Ability to understand, reproduce, and implement methods described in research papers.
Strong analytical and problem-solving skills.
Curiosity and willingness to explore new technologies.
Passion for AI, Machine Learning, and product development.
Ownership mindset and accountability for delivered solutions.
Ability to work independently and contribute within a collaborative team.
Interest in building practical products rather than focusing only on model experimentation.
Comfortable working in a fast-moving and research-driven environment.
Nice to Have
Experience with one or more of the following technologies would be considered an advantage:
YOLO
GroundingDINO
SAM or SAM2
Florence-2
Whisper
Qwen2.5-VL
LLaVA
InternVL
CUDA
TensorRT
ONNX
FastAPI
(Exceptional fresh graduates may also be considered if they can demonstrate strong technical knowledge through relevant academic work, personal projects, GitHub repositories, Kaggle participation, research, or a technical portfolio.)