Job Description: Data Scientist (MLOps, CI/CD, Generative AI)Experience : 4 to 6 YearsLocation: Isb / HybridAbout the RoleWe are seeking a Full Stack Data Scientist with strong expertise in MLOps, CI/CD, and Generative AI to join our growing AI/ML team. This role is highly dynamic and requires a balance of technical excellence, customer-facing skills, and business acumen.You will be responsible for the end-to-end machine learning lifecyclefrom data exploration and model development to deployment and optimization. A key part of the role is engaging with customers, understanding their business challenges, and delivering impactful Proof of Concepts (POCs) that build trust and demonstrate value.Candidates with hands-on expertise in statistical models, traditional ML, deep learning architectures, time series forecasting, and financial modeling will be strongly preferred.Experience with GPUs, CUDA, and high-performance computing is a plus, given the scale and complexity of deep learning and Generative AI workloads.Key ResponsibilitiesEngage directly with customers to understand business problems and translate them into data science solutions.Design and deliver impactful projects that clearly demonstrate value and help win new business opportunities.Build and deploy end-to-end ML/AI solutions across domains such as NLP, Computer Vision, Generative AI, and Forecasting.Develop and optimize MLOps pipelines for model training, deployment, and monitoring with CI/CD best practices.Implement statistical, traditional ML, and deep learning models, ensuring accuracy, scalability, and robustness.Create time series and financial forecasting models for predictive analytics in business and finance use cases.Apply Generative AI methods (LLMs, RAG, LangChain, Hugging Face, Diffusion Models) to enterprise use cases.Optimize training and inference with GPU acceleration and CUDA where applicable.Ensure production-grade deployment with monitoring, drift detection, and retraining strategies.Collaborate with product managers, engineers, and stakeholders to align technical solutions with business outcomes.Stay updated with industry trends and bring innovative AI/ML solutions to customer engagements.Required QualificationsEducation: Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or related field.Experience: 3 years of professional experience in end-to-end ML/AI solution delivery.Technical Expertise:Statistical models (hypothesis testing, regression, time series analysis).Traditional ML (SVM, decision trees, ensemble methods, clustering, recommendation systems).Deep Learning (CNNs, RNNs, LSTMs/GRUs, Transformers, GANs, Diffusion Models).Time Series Financial Models (ARIMA, Prophet, advanced LSTM/GRU models, risk prediction).Generative AI (LLMs, RAG, LangChain, Hugging Face Transformers, OpenAI APIs).Proficiency in Python (NumPy, Pandas, Scikit-learn, Statsmodels, TensorFlow, PyTorch).Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI).Strong knowledge of CI/CD workflows (GitHub Actions, GitLab CI, Jenkins, Azure DevOps).Experience with cloud platforms (AWS, Azure, GCP) for ML deployment and scaling.Strong understanding of Docker, Kubernetes, and production deployment.Excellent communication and presentation skills to face customers confidently.Proven ability to translate customer requirements into POCs and production solutions..Preferred/Bonus SkillsExperience with GPUs, CUDA, and high-performance model training.Familiarity with real-time inference frameworks (TensorRT, Triton, TorchServe, FastAPI).Knowledge of feature stores (Feast, Tecton) and monitoring tools (Evidently, WhyLabs, Prometheus, Grafana).Exposure to financial services, supply chain, or enterprise AI domains.Track record of winning client trust through successful POCs and solution delivery.Contributions to open-source ML/AI projects. Job Description: Data Scientist (MLOps, CI/CD, Generative AI)Experience : 4 to 6 YearsLocation: Isb / HybridAbout the Role Job Description: Data Scientist (MLOps, CI/CD, Generative AI)Experience : 4 to 6 YearsLocation: Isb / Hybrid We are seeking a Full Stack Data Scientist with strong expertise in MLOps, CI/CD, and Generative AI to join our growing AI/ML team. This role is highly dynamic and requires a balance of technical excellence, customer-facing skills, and business acumen. You will be responsible for the end-to-end machine learning lifecyclefrom data exploration and model development to deployment and optimization. A key part of the role is engaging with customers, understanding their business challenges, and delivering impactful Proof of Concepts (POCs) that build trust and demonstrate value.

Engage directly with customers to understand business problems and translate them into data science solutions. Engage directly with customers to understand business problems and translate them into data science solutions. Engage directly with customers to understand business problems and translate them into data science solutions. Design and deliver impactful projects that clearly demonstrate value and help win new business opportunities. Design and deliver impactful projects that clearly demonstrate value and help win new business opportunities. Design and deliver impactful projects that clearly demonstrate value and help win new business opportunities. Build and deploy end-to-end ML/AI solutions across domains such as NLP, Computer Vision, Generative AI, and Forecasting. Build and deploy end-to-end ML/AI solutions across domains such as NLP, Computer Vision, Generative AI, and Forecasting. Build and deploy end-to-end ML/AI solutions across domains such as NLP, Computer Vision, Generative AI, and Forecasting. Develop and optimize MLOps pipelines for model training, deployment, and monitoring with CI/CD best practices. Develop and optimize MLOps pipelines for model training, deployment, and monitoring with CI/CD best practices. Develop and optimize MLOps pipelines for model training, deployment, and monitoring with CI/CD best practices. Implement statistical, traditional ML, and deep learning models, ensuring accuracy, scalability, and robustness. Implement statistical, traditional ML, and deep learning models, ensuring accuracy, scalability, and robustness. Implement statistical, traditional ML, and deep learning models, ensuring accuracy, scalability, and robustness. Create time series and financial forecasting models for predictive analytics in business and finance use cases. Create time series and financial forecasting models for predictive analytics in business and finance use cases. Create time series and financial forecasting models for predictive analytics in business and finance use cases. Apply Generative AI methods (LLMs, RAG, LangChain, Hugging Face, Diffusion Models) to enterprise use cases. Apply Generative AI methods (LLMs, RAG, LangChain, Hugging Face, Diffusion Models) to enterprise use cases. Apply Generative AI methods (LLMs, RAG, LangChain, Hugging Face, Diffusion Models) to enterprise use cases. Optimize training and inference with GPU acceleration and CUDA where applicable. Optimize training and inference with GPU acceleration and CUDA where applicable. Optimize training and inference with GPU acceleration and CUDA where applicable. Ensure production-grade deployment with monitoring, drift detection, and retraining strategies. Ensure production-grade deployment with monitoring, drift detection, and retraining strategies. Ensure production-grade deployment with monitoring, drift detection, and retraining strategies. Collaborate with product managers, engineers, and stakeholders to align technical solutions with business outcomes. Collaborate with product managers, engineers, and stakeholders to align technical solutions with business outcomes. Collaborate with product managers, engineers, and stakeholders to align technical solutions with business outcomes. Stay updated with industry trends and bring innovative AI/ML solutions to customer engagements. Stay updated with industry trends and bring innovative AI/ML solutions to customer engagements. Stay updated with industry trends and bring innovative AI/ML solutions to customer engagements.

Education: Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or related field. Education: Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or related field. Education: Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or related field. Experience: 3 years of professional experience in end-to-end ML/AI solution delivery. Experience: 3 years of professional experience in end-to-end ML/AI solution delivery. Experience: 3 years of professional experience in end-to-end ML/AI solution delivery. Technical Expertise: Technical Expertise: Technical Expertise: Statistical models (hypothesis testing, regression, time series analysis). Statistical models (hypothesis testing, regression, time series analysis). Statistical models (hypothesis testing, regression, time series analysis). Traditional ML (SVM, decision trees, ensemble methods, clustering, recommendation systems). Traditional ML (SVM, decision trees, ensemble methods, clustering, recommendation systems). Traditional ML (SVM, decision trees, ensemble methods, clustering, recommendation systems). Deep Learning (CNNs, RNNs, LSTMs/GRUs, Transformers, GANs, Diffusion Models). Deep Learning (CNNs, RNNs, LSTMs/GRUs, Transformers, GANs, Diffusion Models). Deep Learning (CNNs, RNNs, LSTMs/GRUs, Transformers, GANs, Diffusion Models). Time Series Financial Models (ARIMA, Prophet, advanced LSTM/GRU models, risk prediction). Time Series Financial Models (ARIMA, Prophet, advanced LSTM/GRU models, risk prediction). Time Series Financial Models (ARIMA, Prophet, advanced LSTM/GRU models, risk prediction). Generative AI (LLMs, RAG, LangChain, Hugging Face Transformers, OpenAI APIs). Generative AI (LLMs, RAG, LangChain, Hugging Face Transformers, OpenAI APIs). Generative AI (LLMs, RAG, LangChain, Hugging Face Transformers, OpenAI APIs). Proficiency in Python (NumPy, Pandas, Scikit-learn, Statsmodels, TensorFlow, PyTorch). Proficiency in Python (NumPy, Pandas, Scikit-learn, Statsmodels, TensorFlow, PyTorch). Proficiency in Python (NumPy, Pandas, Scikit-learn, Statsmodels, TensorFlow, PyTorch). Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI). Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI). Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI). Strong knowledge of CI/CD workflows (GitHub Actions, GitLab CI, Jenkins, Azure DevOps). Strong knowledge of CI/CD workflows (GitHub Actions, GitLab CI, Jenkins, Azure DevOps). Strong knowledge of CI/CD workflows (GitHub Actions, GitLab CI, Jenkins, Azure DevOps). Experience with cloud platforms (AWS, Azure, GCP) for ML deployment and scaling. Experience with cloud platforms (AWS, Azure, GCP) for ML deployment and scaling. Experience with cloud platforms (AWS, Azure, GCP) for ML deployment and scaling. Strong understanding of Docker, Kubernetes, and production deployment. Strong understanding of Docker, Kubernetes, and production deployment. Strong understanding of Docker, Kubernetes, and production deployment. Excellent communication and presentation skills to face customers confidently. Excellent communication and presentation skills to face customers confidently. Excellent communication and presentation skills to face customers confidently. Proven ability to translate customer requirements into POCs and production solutions.. Proven ability to translate customer requirements into POCs and production solutions.. Proven ability to translate customer requirements into POCs and production solutions.. Preferred/Bonus Skills Experience with GPUs, CUDA, and high-performance model training. Experience with GPUs, CUDA, and high-performance model training. Experience with GPUs, CUDA, and high-performance model training. Familiarity with real-time inference frameworks (TensorRT, Triton, TorchServe, FastAPI). Familiarity with real-time inference frameworks (TensorRT, Triton, TorchServe, FastAPI). Familiarity with real-time inference frameworks (TensorRT, Triton, TorchServe, FastAPI). Knowledge of feature stores (Feast, Tecton) and monitoring tools (Evidently, WhyLabs, Prometheus, Grafana). Knowledge of feature stores (Feast, Tecton) and monitoring tools (Evidently, WhyLabs, Prometheus, Grafana). Knowledge of feature stores (Feast, Tecton) and monitoring tools (Evidently, WhyLabs, Prometheus, Grafana). Exposure to financial services, supply chain, or enterprise AI domains. Exposure to financial services, supply chain, or enterprise AI domains. Exposure to financial services, supply chain, or enterprise AI domains. Track record of winning client trust through successful POCs and solution delivery. Track record of winning client trust through successful POCs and solution delivery. Track record of winning client trust through successful POCs and solution delivery. Contributions to open-source ML/AI projects. Contributions to open-source ML/AI projects. Contributions to open-source ML/AI projects.

📊 Experience Required: 3+ years of professional experience

⭐ Seniority Level: Entry level

🎯 Job Function: Sales, General Business, and Education

See who Datamatics Technologies has hired for this role

🏭 Industries: Wireless Services, Telecommunications, and Communications Equipment Manufacturing

👥 Number of Applicants: 25

📅 Posted Date: December 17, 2025