Senior AI Engineer

About Centific Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to create contextual, multilingual, pre‑trained datasets;

fine‑tuned, industry‑specific LLMs; and RAG pipelines supported by vector databases. Our zero‑distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster. Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.

Centific’s Physical AI team is building next‑generation AI systems at the intersection of Vision AI, multimodal foundation models, agentic AI, simulation, and real‑world robotics. We work on practical and frontier problems spanning video understanding, autonomous systems, embodied intelligence, data pipelines, evaluation, and deployment. We are looking for an AI Engineer who can bridge research and production: someone who can build, fine‑tune, evaluate, and deploy AI systems across vision, language, video, simulation, and agentic workflows.

You will work closely with research, data science, and platform engineering teams to turn advanced AI ideas into scalable, customer‑ready systems. This role is ideal for an engineer with strong hands‑on experience in modern AI/ML systems, a solid grasp of multimodal and agentic architectures, and an interest in Physical AI challenges such as perception, dexterity, navigation, simulation, and autonomous decision‑making.

• Design, build, and deploy AI/ML systems across Vision AI, multimodal AI, agentic AI, and Physical AI use cases.
• Develop and integrate models for video understanding, image perception, tracking, multimodal reasoning, autonomous workflows, and robotics‑related tasks.
• Work with research and engineering teams to product ionize models using platforms such as NVIDIA NeMo, Riva, RAPIDS, Triton, Isaac stack, AWS Bedrock, GCP Vertex AI, and related SDKs.
• Build pipelines for large‑scale structured and unstructured data, including video, audio, sensor, and text data.
• Implement and optimize model inference, evaluation, monitoring, drift detection, and governance workflows in production environments.
• Support experimentation with LLMs, VLMs, world models, agent frameworks, tool‑using agents, and memory‑enabled agentic systems.
• Contribute to AI systems for simulation, digital twins, robotics perception, dexterous manipulation, long‑horizon task execution, autonomous driving, and edge‑case evaluation.
• Perform data analysis, error analysis, benchmarking, and model improvement for robustness, safety, and generalization.
• Collaborate directly with customers and internal teams to identify relevant datasets, define success metrics, and translate business needs into AI system design.
• Help build reusable internal frameworks, accelerators, and data products for multimodal and agentic AI deployments.

• Master’s degree in Computer Science, Machine Learning, or equivalent practical experience.
• 5+ years of experience building and deploying large scale AI/ML systems in production.
• Strong programming skills in Python and solid experience with modern ML frameworks such as PyTorch, Tensor Flow, or JAX.
• Hands‑on experience with Vision AI, including one or more of: image/video models, object detection, tracking, segmentation, grounding, video analytics, 3D vision, or multimodal perception.
• Experience with Generative AI, including LLMs, VLMs, multimodal pipelines, RAG, agents, or agent orchestration…