Note: The job is a remote job and is open to candidates in USA. Algotale Group is seeking a Data Scientist with a strong background in building AI/ML systems and practical experience in Generative AI applications. The role involves developing scalable application architectures, optimizing LLM-based applications, and collaborating with cross-functional teams to deliver effective solutions.
Responsibilities
- 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
Skills
- 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
- 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
Company Overview