<p><p><b>Role Overview :</b></p><p><p><b><br/></b></p>We are seeking passionate and experienced professionals in Generative AI (GenAI) to join our AI & Data Science team.
The ideal candidate will have deep hands-on expertise in working with LLMs, RAG pipelines, LangChain, and cloud AI services.
You will be responsible for designing, developing, and deploying GenAI solutions for real-world enterprise use cases.<br/><br/><b>Key Responsibilities :</b></p><p><p><b><br/></b></p>- Architect and implement LLM-based agent frameworks to support task automation and intelligent decision-making.</p><p><br/>- Develop and optimize RAG (Retrieval-Augmented Generation) systems using efficient chunking strategies for context-aware responses.<br/><br/></p><p>- Use LangChain to orchestrate modular GenAI workflows integrated with vector databases (e.g., FAISS, Pinecone, Chroma).<br/><br/></p><p>- Build and maintain knowledge graphs and incorporate Vision APIs for multimodal intelligence.<br/><br/></p><p>- Apply advanced prompt engineering and token management to control costs and improve LLM accuracy.<br/><br/></p><p>- Ensure reliability and robustness through hallucination control methodologies and responsible AI practices.<br/><br/></p><p>- Design and deploy scalable GenAI applications on cloud platforms (Azure, AWS, or GCP).<br/><br/></p><p>- Collaborate with cross-functional teams including data scientists, cloud engineers, and domain experts to integrate GenAI into products.<br/><br/></p><p>- Leverage tools like Docker for containerization and Git for code versioning.<br/><br/></p><p>- Use SQL and Python to process and manipulate data effectively.<br/><br/><b>Must-Have Skills :</b></p><p><p><b><br/></b></p>- Agent Frameworks (e.g., LangChain Agents)<br/><br/></p><p>- Retrieval-Augmented Generation (RAG)<br/><br/></p><p>- Chunking Strategies for document processing<br/><br/></p><p>- Deep understanding of LLMs (e.g., OpenAI, Anthropic, Meta, Cohere)<br/><br/></p><p>- Experience with AI services on Azure, AWS, or GCP (at least one)<br/><br/></p><p>- LangChain framework<br/><br/></p><p>- Vector Databases (e.g., FAISS, Pinecone, Weaviate)<br/><br/></p><p>- Token Management strategies<br/><br/></p><p>- Knowledge Graphs (e.g., Neo4j, RDF-based models)<br/><br/></p><p>- Vision APIs (e.g., Google Vision, AWS Rekognition, Azure Computer Vision)<br/><br/></p><p>- Prompt Engineering expertise<br/><br/></p><p>- Proficiency in Python<br/><br/><b>Good-to-Have Skills :</b></p><p><p><b><br/></b></p>- AI/ML Algorithms and optimization<br/><br/></p><p>- Deep Learning frameworks (e.g., PyTorch, TensorFlow)<br/><br/></p><p>- Computer Vision models and techniques<br/><br/></p><p>- Hallucination Control Techniques<br/><br/></p><p>- Responsible AI frameworks and compliance methodologies<br/><br/><b>Technical Proficiencies :</b></p><p><p><b><br/></b></p>- Programming : Python (mandatory)<br/><br/></p><p>- Cloud : Azure, AWS, or GCP (at least one is mandatory)<br/><br/></p><p>- Containers : Docker<br/><br/></p><p>- Database : SQL<br/><br/></p><p>- Version Control : Git or similar<br/><br/><b>Qualifications :</b></p><p><p><b><br/></b></p>- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.<br/><br/></p><p>- Relevant certifications in AI/ML or Cloud Platforms are a plus.<br/><br/></p><p>- Strong analytical thinking, problem-solving, and communication skills.</p><br/></p> (ref:hirist.tech)