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.