FULLTIME
AI/ML Engineer
Technource
Not specified · onsite · Posted 71d ago
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Undisclosed1–2 yrsfulltimeNot specified
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Section · 01
About this role
Position AI/ML Engineer Industry Type IT Services
Experience 1+ Years Salary As per industry standard Job Location Ahmedabad
Job Description Technource is hiring a
skilled AI/ML Engineer to build and deploy advanced
machine learning and
deep learning solutions across real-world systems. The ideal candidate will have hands-on experience with the
full model lifecycle , be capable of working on
face and object recognition , and integrate models into
high-performance, real-time applications . Describe Skill and Experience
- Design, develop, and deploy ML/DL models for classification, regression, clustering, recommendation, and real-time applications.
- Manage end-to-end model lifecycle: data preparation, preprocessing, augmentation, training, validation, deployment, and monitoring.
- Build and optimize RAG-based architectures by integrating LLMs (e.g., GPT, LLaMA) with vector databases and external data sources.
- Develop and deploy accurate face recognition systems (e.g., FaceNet, ArcFace) and object detection with liveness detection.
- Prepare custom datasets, apply data augmentation, and validate models for robustness and accuracy.
- Deploy models using REST APIs, Docker, Kubernetes, and MLOps tools for scalable, production-grade solutions.
- Ensure real-time performance of AI models in production systems, including edge deployment when required.
- Strong skills in Python, OpenCV, NumPy, and either PyTorch or TensorFlow.
- Excellent debugging, optimization, and problem-solving skills on real-world datasets.
- Familiarity with model optimization techniques and performance scaling is a plus.
Sourced from linkedin
Section · 02
Skills
DockerKubernetesRetrieval Augmented GenerationLlamaMLOpsOpencvvector databasesFace RecognitionObject RecognitionPytorchTensorflowPythonNumpyREST APIDeep LearninggptMachine LearningLarge Language Models (LLMs)Data AugmentationFaceNetArcFaceLiveness DetectionObject DetectionRecommendation Systemsregression algorithmsClassification AlgorithmsClustering TechniquesML/DL ModelsModel OptimizationProblem SolvingModel DeploymentModel Monitoringdata preparationregressionclusteringoptimizationclassificationModel Validationdata preprocessingModel TrainingReal-time ApplicationsdebuggingModel lifecycle managementEdge DeploymentArtificial IntelligenceClustering Algorithms