Job Overview

We are looking for a highly skilled and self-motivated Generative AI Engineer with over 6.5+ years of experience to join our dynamic Data Science team. The ideal candidate will bring deep expertise in Generative AI, along with a solid foundation in Data Science. You should have hands-on experience in designing, deploying, and managing Large Language Model (LLM)-based solutions, developing intelligent AI agents, and delivering scalable AI applications on cloud platforms. In this role, you will be instrumental in building end-to-end intelligent systems that drive tangible business impact across diverse domains.

Key Responsibilities

Qualifications

1. Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
2. 6.5+ years of hands-on experience in machine learning and data science.
3. Proficient in Python and ML libraries (scikit-learn, pandas, Hugging Face Transformers, etc.).

Generative AI & LLMs

1. Lead, design, fine-tune, and deploy Large Language Models (LLMs) using platforms such as AWS Bedrock and open-source model hubs.
2. Build and manage autonomous AI agents using frameworks like LangGraph, Crew AI, Phidata, AutoGen, or AWS Bedrock Agents.
3. Implement Retrieval-Augmented Generation (RAG) pipelines with vector databases (e.g., Pinecone, FAISS, PGVector) to enhance contextual responses.
4. Conduct LLM evaluations using prompt engineering techniques and custom fine-tuning to optimize performance for business use cases.
5. Evaluate model performance using appropriate metrics and optimize for accuracy, efficiency, and scalability.
6. Stay updated with the latest research in Gen AI and integrate relevant innovations into ongoing projects.
7. Contribute to model governance, interpretability, and responsible AI practices.

Programming & Software Engineering

1. Write clean, modular, and well-documented Python code, leveraging machine learning libraries and agentic frameworks (e.g., LangGraph, AutoGen, Crew AI).
2. Deploy AI agents on cloud platforms with a focus on user experience, including building lightweight front-end interfaces using tools like Streamlit or Gradio.
3. Use Git for version control, collaborating through pull requests, code reviews, and CI/CD workflows.
4. Adhere to software engineering best practices, including testing, code modularity, and documentation for maintainability and scalability.

Teamwork & Communication

1. Collaborate with cross-functional teams including data engineers, product managers, and business stakeholders to deliver AI-driven solutions.
2. Translate complex technical concepts into clear insights for both technical and non-technical audiences.
3. Manage multiple projects simultaneously, ensuring timely delivery and high-quality outputs.

Must Have – Data Science & NLP

1. Develop and deploy supervised ML models including regression, classification, and time-series forecasting.
2. Apply NLP techniques for text classification, sentiment analysis, and named entity recognition.
3. Use vector embeddings for similarity search and information retrieval tasks.