5 Tensorflow Serving Jobs

Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment Collaborate with data scientists and software engineers to operationalize ML models Develop and maintain CI/CD pipelines for ML workflows Implement monitoring and logging solutions for ML models Optimize ML infrastructure for performance, scalability, and cost-efficiency Ensure compliance with data privacy and security regulations Strong programming skills in Python, with experience in ML frameworks Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes) Proficiency in cloud platform (AWS) and their ML-specific services Experience with MLOps tools Experience with ML model serving frameworks (TensorFlow Serving, TorchServe) Primary Skills Machine Learning CI/CD Pipelines Devops Secondary Skills Good Communication

Strong programming skills in Python or R, with good knowledge in data manipulation, analysis, and visualization libraries (pandas, numpy, matplotlib, seaborn) Knowledge of machine learning techniques algorithms. FMCG industry will be preferable. Required Candidate profile Hands-on knowledge on machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Proficiency in SQL for data extraction, integration, and manipulation to analyze large datasets. Perks and benefits To be disclosed post interview

Sr. AI/ML Python Developer - 5-8 yrs * cross-functional teams on data analysis & statistics. * Develop ML models - Python, NumPy, Pandas, DLS & NLP. * Imple data pipelines, deploy models on TensorFlow Serving & GCP. Drop to rajkalyan@garudaven.com Food allowance Annual bonus Provident fund Health insurance Office cab/shuttle

Job Type: Full-Time Location: Pune & Mumbai (Onsite) Work Timings: 10am to 7pm Experience Required: 2+ Years About the company InCommon is hiring on behalf of Kale Logistics. Kale Logistics Solutions , founded in 2010, is a global cloud-based tech provider serving Fortune 500 companies. Specializing in logistics technology , we offer Cargo Community Platforms that streamline trade-related data exchange. With offices in India, UAE, Kenya, Netherlands, and North America , and 5,500+ clients across 40 countries , we enhance operational efficiency for Logistics Service Providers (LSPs) worldwide. Why Join Kale Logistics Solutions? Impactful Work Be part of a team shaping the future of logistics technology. Innovation-Driven Culture Work on cutting-edge cloud-based enterprise solutions. Global Exposure Collaborate with teams and clients across multiple countries. Growth & Learning Opportunities for professional development and career progressive Position Summary We are looking for a highly skilled AI Engineer to lead the development of innovative Generative and Agentic AI applications. In this role, you will design and integrate AI solutions that leverage state-of-the-art models into web-based applications. You will collaborate with cross-functional teams to build, deploy, and scale AI-powered solutions that drive business value. If you are eager to work with new technologies, explore emerging trends in AI, and contribute to transformative projects, we want you on our team! Key Responsibilities Develop and deploy scalable AI-powered applications using the Python stack. Leverage cutting-edge AI/ML technologies such as LangChain, LangGraph, AutoGen, Phidata, CrewAI Hugging Face, OpenAI APIs, PyTorch, TensorFlow, and other advanced frameworks to build innovative AI applications. Write clean, efficient, and well-documented code adhering to best practices. Build and manage robust APIs for integrating AI solutions into applications. Research and experiment with emerging technologies to discover new AI-driven use cases. Deploy and manage AI solutions in cloud environments (AWS, Azure, GCP), ensuring security, scalability, and performance. Collaborate with product managers, engineers, and UX/UI designers to define AI application requirements and align them with business objectives. Apply MLOps principles to streamline AI model deployment, monitoring, and optimization. Solve complex problems using foundational knowledge of generative AI, machine learning, and data processing techniques. Contribute to continuous improvement of development processes and practices. Resolve production issues by conducting effective troubleshooting and root cause analysis (RCA) within SLAs. Work with operations teams to support product deployment and issue resolution. Requirements Educational Background Bachelors or Masters degree in Computer Science or related fields with a strong academic track record. Experience Technical Skills: 3+ years of hands-on experience in building and deploying AI applications on Python stack. Strong knowledge of Python and related frameworks. Good knowledge of few of the AI/ML frameworks and agentic frameworks and platforms like LangChain, LangGraph, AutoGen, Hugging Face, CrewAI, OpenAI APIs, PyTorch, and TensorFlow etc. Experience with AI/ML workflows, including data preparation, model deployment, and optimization. Proficiency in building and consuming RESTful APIs for connecting AI models with web applications. Knowledge of MLOps tools and practices, including model lifecycle management, CI/CD for AI, and model monitoring. Join us in revolutionizing logistics through cutting-edge technology solutions !

Key Responsibilities: Model Hosting and Deployment: Design, develop, and maintain production-grade servers to host machine learning models. Ensure the models are deployed securely and can be accessed by APIs for multi-user interaction. Server and Infrastructure Management: Build and optimize scalable infrastructure for hosting models. Oversee the deployment and monitoring of ML models in production to ensure high availability and performance. API Development and Management: Develop and deploy RESTful APIs to allow external users and systems to interact with deployed machine learning models. Ensure APIs are performant and reliable under heavy load. Data Handling and Storage: Utilize Redis, PostgreSQL, and other databases for fast and scalable storage and retrieval of model data and predictions. Adapter model handling: Deploy various adapters for tuned models and ensure that model updates and retraining processes are seamless. Integrate new versions of models into existing systems. Documentation: Maintain comprehensive documentation for deployed models, infrastructure setup, and best practices to ensure knowledge sharing and ease of future updates. Required Skills and Experience: Model Hosting and Deployment: Strong experience with deploying and managing machine learning models in production environments. Server Infrastructure: Proficient in building and managing servers to host and serve models for scalable access. APIs: Expertise in developing and deploying RESTful APIs for model inference. Redis & Celery: Solid experience with Redis for caching and Celery for task management and background job processing. PostgreSQL: Strong knowledge of PostgreSQL for data management and querying in the context of ML applications. Model Repositories: Experience with version control and repositories for machine learning models, including integration with Git or similar platforms. CUDA & GPU: Proficient in using CUDA for accelerating machine learning workloads, especially on GPU-enabled machines. Cloud Platforms: Experience with cloud services (e.g., AWS, GCP, Azure) for deploying and managing models at scale. Preferred Skills: Familiar with CI/CD pipelines for ML models to ensure automated testing, deployment, and updates. Experience with containerization technologies such as Docker and Kubernetes for deployment and orchestration. Familiarity with frameworks like TensorFlow Serving, TorchServe, or similar for serving models in production. Knowledge of model monitoring tools and logging frameworks. Ability to work in a fast-paced, collaborative environment with cross-functional teams. Knowledge in consumer and server grade GPU to handle CUDA compatibility issues.