Fullstack Data Scientist

Job Description: Full Stack Data Scientist (MLOps, CI/CD, Generative AI)

About the Role

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 lifecycle—from 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 Responsibilities

·       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.

·       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 Qualifications

·       Education: Bachelor's or Master's 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:

o  Statistical models (hypothesis testing, regression, time series analysis).

o  Traditional ML (SVM, decision trees, ensemble methods, clustering, recommendation systems).

o  Deep Learning (CNNs, RNNs, LSTMs/GRUs, Transformers, GANs, Diffusion Models).

o  Time Series & Financial Models (ARIMA, Prophet, advanced LSTM/GRU models, risk prediction).

o  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 Skills

·       Experience 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.