AI/ML Engineer, Synthetic Data & Simulation (Hyderabad)

Role Overview Were 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. Youll 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 current 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 - 36 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, videosynthesis 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 Role Overview Were 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. Youll 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 current 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 - 36 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, videosynthesis 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