5 Experiment Tracking Jobs

As a Lead Data Scientist specializing in Natural Language Processing (NLP) and Computer Vision (OpenCV), you will play a crucial role in driving our AI initiatives. Your primary responsibilities will include designing, developing, and deploying cutting-edge NLP and computer vision models to address real-world challenges across various data domains such as text, image, and structured data. Your expertise will be instrumental in setting the technical direction, guiding junior team members, and enhancing our data science capabilities organization-wide. A key aspect of your role will involve hands-on leadership in research and development using advanced ML/DL techniques. You will lead by example in mentoring junior and mid-level data scientists, contributing to their professional growth. Collaboration with cross-functional teams, including Engineering, Product, and Business units, will be essential as you drive high-impact AI projects and shape the strategic roadmap for data science within the company. Your proficiency in applying advanced NLP techniques with tools like spaCy, HuggingFace Transformers, BERT, and GPT will be critical in delivering successful outcomes. Additionally, your experience in solving computer vision challenges using OpenCV, PyTorch, and TensorFlow will be invaluable in driving innovation and performance tuning initiatives. Working with cloud platforms such as Azure and AWS for model training, deployment, and monitoring will also be part of your regular tasks. To excel in this role, you should possess at least 6 years of hands-on experience in data science, with a specific focus on NLP and computer vision for at least 4 years. Strong Python skills, familiarity with ML fundamentals, and experience in deploying ML models in cloud environments are prerequisites. Your ability to communicate effectively in English, both verbally and in writing, will be essential for engaging with technical and non-technical stakeholders across different geographies. In this role, you will have the opportunity to be part of a mission-driven company that values innovation, autonomy, and collaboration. With a clear path to grow into a Head of Data Science position, you can lead meaningful AI projects and contribute directly to the success of the organization. The role offers a flexible remote work environment with monthly in-person collaboration in Indore and occasional international travel, providing a dynamic and forward-thinking work experience where your contributions make a tangible impact on company growth.,

As a Senior CV Engineer at our esteemed client, a Series-A funded deep-tech company in Gurugram specializing in AI Product industry, you will be responsible for leading the design and implementation of complex CV pipelines including object detection, instance segmentation, and industrial anomaly detection. Your role will involve owning major modules from concept to deployment to ensure low latency and high reliability of the system. You will be expected to transition algorithms from Python/PyTorch to optimized C++ edge GPU implementations using technologies like TensorRT, ONNX, and GStreamer. Collaboration with cross-functional teams to refine technical strategies and roadmaps will be a key part of your responsibilities. Additionally, you will drive long-term data and model strategies such as synthetic data generation and validation frameworks. Mentoring engineers and upholding high engineering standards will also be a crucial aspect of this role. To be successful in this position, you should have a minimum of 6-10 years of experience in architecting and deploying CV systems. Expertise in multi-object tracking, object detection, and semi/unsupervised learning is essential. Proficiency in Python, PyTorch/TensorFlow, Modern C++, CUDA, and experience with real-time, low-latency model deployment on edge devices are required skills. Strong systems-level design thinking across ML lifecycles and familiarity with MLOps (CI/CD for models, versioning, experiment tracking) are also expected. A Bachelors/Masters degree in CS, EE, or related fields with strong ML and algorithmic foundations is necessary for this role. Preferred qualifications include experience with NVIDIA DeepStream, GStreamer, LLMs/VLMs, and open-source contributions. If you are passionate about cutting-edge technology and have a drive for innovation, this position offers an exciting opportunity to work on a first-of-its-kind app-based operating system for Computer Vision.,

As a member of the AI Research team, you will drive the next wave of generative and multimodal intelligence to empower our autonomous drywall-finishing robots. Your main focus will involve turning cutting-edge vision-language and diffusion advances into robust, real-time systems that have the capability to see, reason, and act on dynamic construction sites. Your responsibilities will include researching and innovating diffusion-based generative models for photorealistic wall-surface simulation, defect synthesis, and domain adaptation. You will be tasked with architecting and training Vision-Language Models (VLMs) and Vision-Language Alignment (VLA) objectives that establish connections between textual work orders, CAD plans, and sensor data to achieve pixel-level understanding. Additionally, you will lead the development of auto-annotation pipelines that can scale to millions of frames and point-clouds with minimal human effort, utilizing techniques such as active learning, self-training, and synthetic data generation. Furthermore, you will be responsible for optimizing and compressing models for deployment on Jetson-class edge devices under ROS 2. The full lifecycle of the project, from problem definition to production hand-off to perception and controls teams, will be in your hands. You will also be expected to publish internal tech reports and external conference papers, as well as mentor interns and junior engineers. To qualify for this role, you should have at least 3 years of experience in deep-learning R\&D or a Ph.D./M.S. in CS, EE, Robotics, or a related field with a strong publication record. Demonstrated expertise in diffusion models and multimodal transformers/VLMs is essential, along with a proven track record of building large-scale data-centric AI workflows. Proficiency in Python, PyTorch (or JAX), experiment tracking, and scalable training is required, as well as familiarity with edge-AI runtimes and CUDA/C++ performance tuning. Joining our team will offer you the opportunity to own breakthrough technology from conception to deployment on active job-sites. You will collaborate cross-functionally with perception, controls, and product teams, and have the chance to shape an industry by introducing intelligent robots to replace dangerous and repetitive construction labor. We offer a competitive salary, equity, hardware budget, flexible hybrid work arrangements, and a culture that values deep work and rapid iteration. If you possess skills in Vision-Language Models, Vision-Language Alignment, R\&D, hold a Ph.D./M.S. in CS, EE, Robotics, and have experience with TensorRT, ONNX Runtime, and CUDA/C++, then this role might be the perfect fit for you.,

You will be joining our team as a skilled Deep Learning Engineer with expertise in object detection and segmentation models. Your primary responsibilities will include implementing and refining object detection models such as YOLOvX, Faster R-CNN, EfficientDet, SSD, and Mask R-CNN. Additionally, you will work on real-time computer vision applications, optimize performance, annotate and prepare datasets, and collaborate on research and development projects to enhance model performance and robustness. As a Deep Learning Engineer, you will be expected to deploy models using Docker on Linux/Windows systems, with experience in edge deployment considered a plus. It will be essential for you to document code, experiments, and deployment processes while collaborating with cross-functional teams. Strong Python programming skills, knowledge of TensorFlow, PyTorch, OpenCV, and ONNX, as well as hands-on experience with Docker and familiarity with model optimization techniques like quantization and pruning are required for this role. An advantage would be your experience in edge deployments using platforms such as NVIDIA Jetson, TensorRT, and OpenVINO. Additionally, familiarity with experiment tracking tools like MLflow or Weights & Biases is a plus. The qualifications we are looking for include a Bachelors or Masters degree in Computer Science, AI, Data Science, or a related field, along with strong analytical, problem-solving, and team collaboration skills. This is a full-time position with a day shift schedule that requires in-person work at our location.,

At PwC, our data and analytics team focuses on utilizing data to drive insights and support informed business decisions. We leverage advanced analytics techniques to assist clients in optimizing their operations and achieving strategic goals. As a data analysis professional at PwC, your role will involve utilizing advanced analytical methods to extract insights from large datasets, enabling data-driven decision-making. Your expertise in data manipulation, visualization, and statistical modeling will be pivotal in helping clients solve complex business challenges. PwC US - Acceleration Center is currently seeking a highly skilled MLOps/LLMOps Engineer to play a critical role in deploying, scaling, and maintaining Generative AI models. This position requires close collaboration with data scientists, ML/GenAI engineers, and DevOps teams to ensure the seamless integration and operation of GenAI models within production environments at PwC and for our clients. The ideal candidate will possess a strong background in MLOps practices and a keen interest in Generative AI technologies. With a preference for candidates with 4+ years of hands-on experience, core qualifications for this role include: - 3+ years of experience developing and deploying AI models in production environments, alongside 1 year of working on proofs of concept and prototypes. - Proficiency in software development, including building and maintaining scalable, distributed systems. - Strong programming skills in languages such as Python and familiarity with ML frameworks like TensorFlow and PyTorch. - Knowledge of containerization and orchestration tools like Docker and Kubernetes. - Understanding of cloud platforms such as AWS, GCP, and Azure, including their ML/AI service offerings. - Experience with continuous integration and delivery tools like Jenkins, GitLab CI/CD, or CircleCI. - Familiarity with infrastructure as code tools like Terraform or CloudFormation. Key Responsibilities: - Develop and implement MLOps strategies tailored for Generative AI models to ensure robustness, scalability, and reliability. - Design and manage CI/CD pipelines specialized for ML workflows, including deploying generative models like GANs, VAEs, and Transformers. - Monitor and optimize AI model performance in production, utilizing tools for continuous validation, retraining, and A/B testing. - Collaborate with data scientists and ML researchers to translate model requirements into scalable operational frameworks. - Implement best practices for version control, containerization, and orchestration using industry-standard tools. - Ensure compliance with data privacy regulations and company policies during model deployment. - Troubleshoot and resolve issues related to ML model serving, data anomalies, and infrastructure performance. - Stay updated with the latest MLOps and Generative AI developments to enhance AI capabilities. Project Delivery: - Design and implement scalable deployment pipelines for ML/GenAI models to transition them from development to production environments. - Oversee the setup of cloud infrastructure and automated data ingestion pipelines to meet GenAI workload requirements. - Create detailed documentation for deployment pipelines, monitoring setups, and operational procedures. Client Engagement: - Collaborate with clients to understand their business needs and design ML/LLMOps solutions. - Present technical approaches and results to technical and non-technical stakeholders. - Conduct training sessions and workshops for client teams. - Create comprehensive documentation and user guides for clients. Innovation And Knowledge Sharing: - Stay updated with the latest trends in MLOps/LLMOps and Generative AI. - Develop internal tools and frameworks to accelerate model development and deployment. - Mentor junior team members and contribute to technical publications. Professional And Educational Background: - Any graduate / BE / B.Tech / MCA / M.Sc / M.E / M.Tech / Masters Degree / MBA,