Senior Artificial Intelligence/Machine Learning Engineer

Key Responsibilities :

- AI/ML Solution Development : Design, develop, and implement advanced AI/ML solutions, leveraging machine learning algorithms, deep learning frameworks, and statistical models.

- LLM Integration : Build and integrate solutions using Large Language Model (LLM) tools and frameworks, exploring their applications for various business problems.

- Data Pre-processing & Analysis : Apply advanced data pre-processing techniques to clean, transform, and prepare large datasets for model training and evaluation.

- Time-Series & Forecasting : Conduct in-depth time-series analysis and develop robust forecasting models to predict future trends and patterns.

- Model Building & Optimization : Develop, train, and optimize machine learning models, ensuring high accuracy, efficiency, and scalability.

- Programming & Libraries : Write clean, efficient, and well-documented code primarily in Python, utilizing libraries and frameworks such as scikit-learn, pandas, NumPy, and SciPy.

- Experiment Tracking : Utilize experiment-tracking tools like MLflow to manage machine learning lifecycles, track experiments, and reproduce results effectively.

- Statistical Analysis : Apply a strong understanding of statistical analysis, linear algebra, probability theory, and time-series concepts to build rigorous models and interpret results.

- Version Control & Collaboration : Use Git and follow collaborative development workflows to manage code repositories and facilitate team development.

- Troubleshooting & Performance : Identify and resolve performance bottlenecks, debug complex model issues, and optimize AI/ML pipelines for efficiency.

- Research & Innovation : Stay abreast of the latest advancements in AI/ML, particularly in LLMs and time-series analysis, and propose innovative solutions.

Required Skills & Qualifications :

- Experience : 5 to 10 years of professional experience working on AI/ML solutions.

- Machine Learning : Hands-on experience with various machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning).

- Deep Learning : Practical experience with deep learning frameworks (e.g., TensorFlow, PyTorch).

- Data Pre-processing : Strong expertise in data pre-processing techniques.

- Time-Series Analysis : Proven experience in time-series analysis and forecasting.

- LLM Experience : Hands-on experience in building solutions using LLM tools and frameworks (e.g., Hugging Face, OpenAI APIs, LangChain, LlamaIndex).

Programming Proficiency :

- Proficiency in Python is mandatory.

- Strong familiarity with Python libraries and frameworks such as scikit-learn, pandas, NumPy, and SciPy.

- MLOps/Experiment Tracking : Experience using an experiment-tracking tool like MLflow.

- Foundational Knowledge : Strong understanding of statistical analysis, linear algebra, probability theory, and time-series concepts.

- Version Control : Excellent knowledge of version control systems, particularly Git, and collaborative development workflows.

- Problem-Solving : Strong analytical and problem-solving skills.

- Communication : Good verbal and written communication skills.

- Education : Bachelor's or Master's degree in Computer Science,