Algotale(Deqode)- Data Scientist

Requirements
A minimum of 3+ years of Data Science experience, demonstrating strong expertise in building robust AI/ML systems and scalable application architectures.
At least 2+ years of focused, hands-on experience building and deploying practical Generative AI applications, such as AI virtual assistants, complex AI agents, and content generation tools.
Managed and delivered solutions for X+ clients, contributing to ₹Y+ in revenue and ensuring high client satisfaction across multiple verticals.
Spearheaded initiatives that improved operational efficiency and scalability, leading to measurable gains in revenue growth and client acquisition.
Proven experience utilizing core GenAI tools and platforms like AWS Bedrock (including various foundation models and Bedrock Agents), LangChain, designing and implementing RAG pipelines, leveraging Agent-based frameworks (e.g., CrewAI), working with vector databases (e.g., Pinecone, Weaviate, Qdrant, Milvus, ChromaDB), and understanding LLM principles. Experience with model training/fine-tuning (e.g., LoRA/QLoRA) and core NLP concepts is essential.
High proficiency in Python and strong familiarity with modern ML/AI libraries (e.g., Hugging Face Transformers, PyTorch/TensorFlow, Scikit-learn, Pandas, NumPy) and relevant SDKs (e.g., Boto3, OpenAI SDK).
Demonstrated practical experience in developing, performance tuning, and optimizing LLM-based applications, including sophisticated prompt engineering techniques.
Experience leveraging AI coding assistants (e.g., GitHub Copilot, Amazon Q, Cursor) to accelerate development workflows while maintaining code quality.
Deep understanding and practical application of data privacy principles, security best practices, and cost-aware system design, specifically within the context of AI and Generative AI applications.
Strong understanding of working effectively with both structured and unstructured data, including efficient retrieval, processing, and transformation techniques for GenAI.
Proven ability to work effectively across diverse disciplines—collaborating seamlessly with product, design, business, and other engineering teams in fast-paced, dynamic environments.
Hands-on experience developing and deploying applications on cloud platforms, with AWS being highly preferred.
Excellent analytical and problem-solving skills, coupled with a strong bias toward action, experimentation, and continuous learning.
Exceptional communication and collaboration skills, capable of articulating complex technical concepts clearly to various audiences.

What sets you apart:
Active contributions to relevant open-source GenAI tools or frameworks (e.g., LangChain, LlamaIndex, Hugging Face).
A strong portfolio showcasing personal AI side projects or significant contributions to GenAI initiatives.
Exposure to frontend technologies (e.g., React, Streamlit, Gradio) and the ability to contribute across the full stack for end-to-end feature ownership.
Experience with other cloud platforms' GenAI offerings (e.g., Azure OpenAI, Google Vertex AI).
Familiarity with advanced MLOps tools and practices tailored for LLMs.