AI/ML Engineer � Data Simulation & Synthetic Data Generation

About the Role:
Duration: Permanent
Location: Hyderabad
Timings: Full Time (As per company timings)
Notice Period: (Immediate Joiner - Only)
Experience: 3-6 Years
AI/ML Engineer – Data Simulation & Synthetic Data
Generation
Location: HYD, India


Role Overview:
We’re seeking an AI/ML Engineer focused on simulation, synthetic data generation, and
scenario replication. This role involves exploring and evaluating state-of-the-art generative
models, building simulation pipelines, and producing high-quality synthetic datasets
across images, video, audio, and multimodal inputs.
You’ll work on transforming existing real-world samples into diverse, simulated
environments (lighting, weather, backgrounds, noise conditions, poses, domains, etc.) and
scaling data generation pipelines. The ideal candidate is passionate about generative AI,
dataset creation, and model-driven simulation.


Key responsibilities:
​
• Research, evaluate, and benchmark generative and diffusion-based models (Stable
Diffusion, Sora-like models, GANs, NeRFs) for simulation and synthetic data
generation.
• Build pipelines to replicate images/videos across new environments, lighting,
scenes, poses, and object conditions.
• Develop multimodal prompt-based simulation workflows (text → image, image →
image, video → video transformations).
• Fine-tune models for domain-specific simulation tasks: texture transfer,
background replacement, camera simulation, noise injection, motion variation, etc.


• Create automated pipelines to scale image/video/audio/text simulation across
large datasets.
• Evaluate realism, fidelity, annotation consistency, and domain-adaptation
effectiveness of generated data.
• Work with ML researchers to integrate synthetic data into training loops to improve
model performance.
• Collaborate with backend/data teams to design scalable storage, sampling, and
versioning strategies for simulation workflows.
• Develop metrics and QA processes for simulation quality, drift detection, and
dataset reliability.
• Assist in early training pipelines, experiment tracking, and dataset versioning as
simulations grow.


Qualifications:
• 3–6 years of experience in applied machine learning or generative AI.
• Strong Python skills with experience in PyTorch or TensorFlow.


• Hands-on experience with generative models (diffusion models, GANs, video-
synthesis models, NeRFs, etc.).


• Familiarity with data augmentation, image/video transformations, and synthetic
data workflows.
• Experience building pipelines using FastAPI, Airflow, or custom orchestration
frameworks.
• Understanding of GPU-based training/inference and model optimization.
• Practical knowledge of Git, Docker, Linux, and cloud platforms (AWS/GCP/Azure).
Preferred Experience:
• Experience with multimodal generative models (image, video, text-prompted
generation).
• Experience with dataset versioning tools (DVC, W&B, MLflow).
• Understanding of domain adaptation and synthetic-to-real generalization
techniques.