Senior AI Engineer
About The Position
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.
Requirements
- 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 , TensorFlow, 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 frameworks .
- Familiarity with agentic AI concepts such as tool use, planning, workflow orchestration, and memory; experience with agentic memory or knowledge-graph-backed agents is a plus.
- Experience working with NVIDIA AI ecosystem tools such as NeMo, RAPIDS, Riva, Triton , and ideally exposure to Isaac Sim / Omniverse or related simulation environments.
- Experience building scalable inference or training pipelines on GPU infrastructure , with familiarity in performance optimization, distributed systems, or high-performance networking.
- Ability to design experiments, evaluate hypotheses, and implement optimization workflows for real-world AI systems.
- Strong communication skills and ability to work directly with customers, researchers, and cross-functional engineering teams.
Nice To Haves
- Experience with robotics, autonomous driving, simulation, digital twins, or embodied AI systems .
- Familiarity with Ray, Kubernetes, Docker, FastAPI , TensorRT, MLflow/W&B , or related MLOps and distributed AI tooling.
- Exposure to sensor fusion, audio/video analytics, multimodal data pipelines, or robotics data formats .
- Experience with model governance, observability, safety evaluation, or production model monitoring.
- Comfort working across both applied engineering and research-driven prototyping.
Responsibilities
- 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 productionize 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.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
101-250 employees
