Role Overview :
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.
Key Responsibilities :
- Architect and implement LLM-based agent frameworks to support task automation and intelligent decision-making.
- Develop and optimize RAG (Retrieval-Augmented Generation) systems using efficient chunking strategies for context-aware responses.
- Use LangChain to orchestrate modular GenAI workflows integrated with vector databases (e.g., FAISS, Pinecone, Chroma).
- Build and maintain knowledge graphs and incorporate Vision APIs for multimodal intelligence.
- Apply advanced prompt engineering and token management to control costs and improve LLM accuracy.
- Ensure reliability and robustness through hallucination control methodologies and responsible AI practices.
- Design and deploy scalable GenAI applications on cloud platforms (Azure, AWS, or GCP).
- Collaborate with cross-functional teams including data scientists, cloud engineers, and domain experts to integrate GenAI into products.
- Leverage tools like Docker for containerization and Git for code versioning.
- Use SQL and Python to process and manipulate data effectively.
Must-Have Skills :
Agent Frameworks (e.g., LangChain Agents)Retrieval-Augmented Generation (RAG)Chunking Strategies for document processingDeep understanding of LLMs (e.g., OpenAI, Anthropic, Meta, Cohere)Experience with AI services on Azure, AWS, or GCP (at least one)LangChain frameworkVector Databases (e.g., FAISS, Pinecone, Weaviate)Token Management strategiesKnowledge Graphs (e.g., Neo4j, RDF-based models)Vision APIs (e.g., Google Vision, AWS Rekognition, Azure Computer Vision)Prompt Engineering expertiseProficiency in PythonGood-to-Have Skills :
AI / ML Algorithms and optimizationDeep Learning frameworks (e.g., PyTorch, TensorFlow)Computer Vision models and techniquesHallucination Control TechniquesResponsible AI frameworks and compliance methodologiesTechnical Proficiencies :
Programming : Python (mandatory)Cloud : Azure, AWS, or GCP (at least one is mandatory)Containers : DockerDatabase : SQLVersion Control : Git or similarQualifications :
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.Relevant certifications in AI / ML or Cloud Platforms are a plus.Strong analytical thinking, problem-solving, and communication skills.(ref : hirist.tech)