Gen AI & Data Science Engineer

Job Title: Gen AI & Data Science Engineer
Location: Bangalore
Experience: 3 - 5 Years

Job Summary:

We are seeking a highly skilled and passionate GenAI & Data Science Engineer with 3-5 years of experience in Python development, Generative AI, and Data Science. The ideal candidate will have a strong background in AI agent workflows, LLM fine-tuning, and Retrieval-Augmented Generation (RAG) models. You will play a key role in designing, developing, and deploying cutting-edge AI solutions using frameworks such as Lang Chain, Llama Index, and Hugging Face.

This role offers the opportunity to work on transformative AI-driven solutions, leveraging state-of-the-art tools and frameworks to create impactful solutions in real-world applications.

Key Responsibilities:

• Design, develop, and deploy AI solutions with a focus on Generative AI and Data Science.
• Fine-tune Large Language Models (LLM) and implement Retrieval-Augmented Generation (RAG) models.
• Collaborate with cross-functional teams to integrate AI models into business workflows.
• Utilize frameworks such as Lang Chain, Llama Index, and Hugging Face to build scalable AI solutions.
• Participate in end-to-end AI model development, including data preprocessing, model selection, training, evaluation, and deployment.
• Continuously monitor and optimize the performance of AI models to ensure they meet business requirements.
• Work with stakeholders to understand AI requirements and contribute to solution design and architecture.
• Stay up to date with the latest advancements in AI technologies and industry trends.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• 3-5 years of professional experience in Python development, AI, and Data Science.
• Proven experience with Generative AI, including fine-tuning LLMs and working with RAG models.
• Hands-on experience with frameworks like Lang Chain, Llama Index, and Hugging Face.
• Strong understanding of machine learning algorithms, deep learning, and natural language processing (NLP).
• Experience in AI model deployment and scaling in production environments.

Technical Skills

• Programming: Python, including libraries like TensorFlow, PyTorch, Pandas, NumPy, etc.
• AI/ML Frameworks: Lang Chain, Llama Index, Hugging Face, etc.
• Machine Learning Algorithms: Supervised and Unsupervised Learning, NLP, Reinforcement Learning.
• Data Engineering: Data preprocessing, data wrangling, ETL processes. Databricks experience.
• Cloud Platforms: AWS, GCP, Azure (experience with AI tools on cloud platforms).
• Version Control: Git, GitHub, GitLab.
• Familiarity with containerization tools like Docker and Kubernetes.

Soft Skills

• Strong problem-solving skills and analytical thinking.
• Excellent communication and collaboration skills.
• Ability to work independently and as part of a team.
• Adaptability to evolving technologies and requirements.
• Strong attention to detail and high quality of work.
• Time management and ability to meet deadlines.

Work Experience

• 3-5 years of experience working in AI, Data Science, or a related field.
• Practical experience in working with Generative AI, LLM fine-tuning, and RAG models.
• Experience with deployment of AI models in cloud environments.
• Proven track record delivering AI-driven solutions to solve real business problems.

Good to Have

• Experience with other AI tools and frameworks like OpenAI GPT, DeepPavlov, or similar.
• Exposure to data integration and API development.
• Knowledge of advanced topics in NLP, such as transformers and attention mechanisms.
• Experience with building AI-powered applications or chatbots.

Compensation & Benefits

• Salary: Competitive base salary based on experience and skills.
• Bonus: Annual performance-based bonus.
• Benefits: Health insurance, paid time off, work-from-home options, and retirement benefits.
• Learning & Development: Access to AI and Data Science training, conferences, and certifications.

Key Performance Indicators (KPIs) & Key Result Areas (KRAs)

KPIs:

• Timely delivery of AI projects and solutions.
• Quality and accuracy of fine-tuned AI models.
• Successful integration of AI solutions into business workflows.
• Continuous improvement in AI model performance (accuracy, speed, scalability).
• Stakeholder satisfaction and feedback on AI-driven solutions.
• Contribution to knowledge sharing and team collaboration.

KRAs:

• AI model development, fine-tuning, and deployment.
• End-to-end ownership of AI solution delivery.
• Collaboration with cross-functional teams to define and implement business requirements.
• Optimization and monitoring of AI solutions in production environments.

Contact: hr@bigtappanalytics.com