685 Data Science Applications jobs in Noida

• Have hands on experience on real time ML Models / Projects
• Coding in Python Language, Machine Learning, Basic SQL, Git, MS Excel
• Experience in using IDE like Jupyter Notebook, Spyder, PyCharm
• Hands on with AWS Services like S3 bucket, EC2, Sagemaker, Step Functions.
• Engage with clients/consultants to understand requirements
• Taking ownership of delivering ML models with high precision outcomes.
• Accountable for high quality and timely completion of specified work deliverables
• Write codes that are well detailed structured and compute efficient

Role OverviewYou will design, optimize, and deploy real-time computer vision and machine learning pipelines for a multi-sensor biometric authentication device. This includes processing high-resolution data from RGB, IR, and depth/thermal sensors, implementing robust anti-spoofing mechanisms, and integrating privacy-preserving encrypted face-matching algorithms (e.g., CKKS/FHE). The role demands expertise in embedded GPU platforms (NVIDIA Jetson series) and the ability to push models to production under strict latency and security constraints.ResponsibilitiesModel Development & OptimizationBuild and fine-tune face detection, alignment, and embedding extraction models for RGB + IR + depth + thermal data.Research and implement spoof detection (e.g., texture analysis, depth cues, challenge-response).Quantize and optimize models for Jetson hardware (Tensor RT, ONNX Runtime, CUDA/Cu DNN).Encrypted Matching PipelineImplement vector encryption (CKKS/FHE) for face embeddings.Develop threshold-based similarity checks in encrypted space (GPU-accelerated).Collaborate on algorithm selection (cosine similarity vs. alternatives) ensuring high precision at 95%+ thresholds.Multi-Sensor Data FusionSynchronize and process input from multiple camera modules.Fuse thermal/IR/depth data to improve accuracy and spoof resistance.Real-Time Performance EngineeringAchieve sub-second processing latency.Optimize compute graph, memory usage, and sensor I/O.Hardware IntegrationWork closely with embedded engineers to integrate ML pipelines into the device’s OS and BSP.R&DStay updated on state-of-the-art in biometric security, encrypted ML, and edge AI.Prototype and test new methods for privacy-preserving identity verification.Required SkillsCore ML / CV SkillsStrong background in computer vision (Open CV, Py Torch/Tensor Flow).Experience with face recognition systems (e.g., Arc Face, Face Net) and anti-spoofing.Model optimization for constrained devices (Tensor RT, pruning, quantization).Embedded AINVIDIA Jetson platform experience (Nano, Xavier, Orin).CUDA, cu DNN, GPU profiling, and performance tuning.Privacy-Preserving MLHands-on with Fully Homomorphic Encryption (CKKS) / SMPC or related frameworks (e.g., Open FHE, SEAL, FIDESlib).Understanding of secure enclaves and encrypted inference.Systems & IntegrationComfortable working with BSP bring-up, camera sensor integration, and custom drivers.Strong Python & C++ skills for production deployment.BonusDepth/thermal imaging experience.Knowledge of biometric standards & certification (ISO/IEC 19794-5).Background in security for identity systems.Ideal CandidateHas deployed ML models on embedded GPU hardware.Has worked on biometric authentication or high-security identity verification projects.Can own the full stack of data processing — from raw sensor input to encrypted decision output.Thrives in R&D-heavy, rapid-prototyping environments.

Position- Machine Learning EngineerExperience - 3 to 5 yearsJob Location - Pune / RemoteImmediate Joiner OnlyRole OverviewThe ML Ops Engineer plays a critical role in the development, automation, and deployment of Machine Learning (ML) and Generative AI (Gen AI) pipelines across AWS cloud environments. This hands-on role emphasizes building reproducible workflows, integrating observability tools, and enabling efficient, scalable model delivery. The position supports AI systems deployed in banking environments, where resilience and reliability are paramount.Key Experience:3-5 years Strong programming skills in Python, with experience in pandas, SQL, and ML frameworks (e.g., scikit-learn).Should have used Python in ML domain to build, train and maintain models.Familiarity with AWS services such as Lambda, Glue, Cloud Watch, and Bedrock.Experience with container workflows (Docker) and model lifecycle management.Foundational knowledge of observability practices and model deployment fundamentals.Interest or experience in supporting AI systems used by developers or analysts.Strong communication and documentation skills with a collaborative team mindset.Ability to assume ownership of assignments and consistently meet deadlines.Responsibilities:Deploy and maintain ML and Gen AI models using AWS services, including Sage Maker, Fargate, and Bedrock.Apply prompt engineering techniques to optimize Gen AI model performance and reliability.Experience with Retrieval-Augmented Generation (RAG) applications is a plus.Assist in building and maintaining internal model-serving platforms to support development teams.Implement containerized services using Docker and deploy them to AWS infrastructure.Write Infrastructure-as-Code (Ia C) using Terraform to automate cloud resource provisioning (nice to have).Participate in unit and end-to-end testing of ML pipelines, services, and monitoring workflows.Support model monitoring and health tracking using AWS Cloud Watch and internal observability tools.Document internal systems and operational processes to ensure maintainability and reproducibility.