Key Responsibilities:
AI & Machine Learning Solution Development
- Design, develop, and deploy ML and GenAI solutions using GPT/LLM-based architectures.
- Build applications such as chatbots, QA systems, recommendation engines, and intelligent automation tools.
- Work on Agentic AI and Graph-based RAG solutions for enterprise use cases.
- Perform feature engineering, data preprocessing, and model selection to improve accuracy and performance.
Model Development & Optimization
- Develop and fine-tune LLMs using techniques such as hyperparameter tuning and prompt optimization.
- Apply statistical and machine learning techniques including regression (linear & logistic), classification models, and probabilistic graph models.
- Implement forecasting models such as ARIMA, ARIMAX, and exponential smoothing.
- Work with distance metrics such as Euclidean, Manhattan, and Hamming distance for ML applications.
MLOps & Model Lifecycle Management
- Implement end-to-end ML pipelines including training, deployment, monitoring, and versioning.
- Build CI/CD pipelines for ML and GenAI systems using tools like Kubeflow and BentoML.
- Ensure continuous model evaluation using tools like Evidently AI and Great Expectations.
- Maintain model performance through monitoring, feedback loops, and iterative improvements.
Data Engineering & Processing
- Design and manage scalable data pipelines using big data tools such as Databricks and Spark.
- Work with structured and unstructured data for AI model training and evaluation.
- Ensure data quality, integrity, and reliability for ML systems.
Tools, Frameworks & Technologies
- Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, MXNet, CNTK.
- Programming: Python, SQL, PySpark, R, SAS/SPSS.
- AI/ML Platforms: GPT, LLM ecosystems, RAG architectures, Agentic AI frameworks.
- MLOps Tools: Kubeflow, BentoML, Evidently AI, Great Expectations.
- Statistical Tools: Regression, hypothesis testing (T-test, Z-test), probabilistic modeling.
Leadership & Strategy
- Collaborate with cross-functional teams to define AI/GenAI product requirements.
- Define and align AI strategy with senior leadership for short- and long-term goals.
- Mentor and guide data science teams in ML, GenAI, and MLOps practices.
- Drive innovation and adoption of AI-driven solutions across business domains.
Collaboration & Stakeholder Management
- Work closely with product, engineering, and business teams to deliver AI solutions.
- Translate business requirements into scalable AI and ML systems.
- Communicate insights and model outcomes to technical and non-technical stakeholders.
