Technical Lead - Python Programming (6-10 yrs)

Job Description :

We are seeking an experienced Tech Lead - Python (AI/ML) to lead the design, development, and implementation of advanced AI and machine learning systems. The ideal candidate will have a deep technical background in Python and data science libraries, strong expertise in AI and ML algorithms, and hands-on experience in architecting scalable AI solutions. This role requires a mix of technical proficiency, leadership, and innovative thinking to advance our AI capabilities.

Key Responsibilities :

Data Preparation & Analysis :

- Identify, clean, and summarize complex datasets from disparate sources to enable high-quality analyses and insights.

Scripting & Automation :

- Develop Python/PySpark scripts for data processing and transformation, leveraging AWS Glue, Lambda, and other serverless architectures.

Machine Learning Expertise :

- Apply advanced machine learning techniques including Bayesian methods, time series forecasting, regression, clustering, decision trees, and deep learning algorithms using frameworks like PyTorch and TensorFlow.

Model Development & Validation :

- Design and fine-tune machine learning models, ensuring high accuracy and efficiency. Perform robust model validation using state-of-the-art methods.

Data Engineering :

- Build efficient data pipelines to process and transform raw data for use in modeling and analytics.

Distributed Systems Proficiency :

- Utilize distributed databases and frameworks (Map/Reduce, Hadoop, Hive) for large-scale data processing.

Advanced ML Libraries :

- Leverage computational and statistical libraries such as Spark MLlib, Scikit-learn, SciPy, StatsModels, SAS, and R for model development and deployment.

AI Systems Design :

- Lead the design and architecture of AI systems with a strong focus on Retrieval-Augmented Generation (RAG) techniques, large language models, and other machine learning algorithms.

RAG System Implementation :

- Develop and fine-tune RAG systems to improve the performance and relevance of AI-driven outputs, seamlessly integrating external data sources into generative models.

Engineering Pipelines :

- Build and maintain pipelines for model training, evaluation, and deployment, ensuring robust and scalable AI solutions, with emphasis on RAG methodologies.

Evaluation & Benchmarking :

- Conduct thorough evaluation and benchmarking of AI models, including RAG systems, to drive data-driven decision-making for model selection.

Collaboration & Integration :

- Collaborate with data scientists, engineers, and product managers to integrate AI models into production, ensuring alignment with business goals and seamless operation.

Innovation & Continuous Learning :

- Stay updated on the latest advancements in AI and machine learning, particularly in RAG and generative models, and incorporate cutting-edge techniques into the team's practices.

Documentation & Standards :

- Develop and maintain documentation for AI systems, ensuring best practices and standards are followed across the organization.

Mentorship & Leadership :

- Provide mentorship to junior AI engineers and data scientists, fostering an environment of continuous learning and knowledge sharing.

Qualifications :

- Total Experience 5-7 years with relevant experience in AI/ML is 2-3 years.

- Proficiency in Python and data science libraries like NumPy, SciPy, Pandas, and Scikit-learn.

- Hands-on experience with PySpark scripting and AWS services such as Glue and Lambda.

- Strong knowledge of Bayesian methods and time series forecasting.

- Expertise in machine learning algorithms, model validation, and deep learning frameworks like PyTorch and TensorFlow.

- Experience in handling structured, unstructured, and semi-structured data.

- Advanced knowledge of distributed databases such as Map/Reduce, Hadoop, Hive.

- Familiarity with RAG systems and large language models for AI-driven outputs.

- Experience with supervised and unsupervised learning techniques, including forecasting, classification, text mining, NLP, and search algorithms.

- Strong collaboration, leadership, and mentorship skills.

Preferred Experience :

- Experience working with Spark MLlib, SciPy, StatsModels, SAS, and R.

- Proven track record in developing and fine-tuning RAG systems for enhancing AI outputs.

- Ability to innovate and apply the latest AI techniques to real-world business problems.