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