
GenAI Developer
- Bangalore, Karnataka
- Permanent
- Full-time
- Design and implement evaluation pipelines for both closed and open-source Large and Small Language Models, capturing key metrics such as performance, latency, cost, hallucination, helpfulness, harmlessness, quantization, and fine-tuning
- Develop advanced Textual Information Retrieval, classification, and clustering algorithms using frameworks like SpaCy, NLTK, and Hugging Face
- Drive world-class implementation of the no-code agentic platform Glean, developing custom agents to serve as AI assistants for employees
- Fine-tune GenAI models to optimize performance, scalability, and reliability
- Support buy vs. build evaluations for GenAI solutions
- Implement prompt engineering best practices to minimize token usage and improve output accuracy; contribute to a centralized prompt library
- Develop robust tools to automate RAG pipeline creation, integrating diverse datasets into vector databases and incorporating knowledge graphs as needed
- Help building conversational tools to answer business questions from structured data systems ( analytical AI or QueryGPT) and applications like ChatGPT for the enterprise
- Conduct research to advance generative AI and apply findings to real-world use cases
- Document processes, models, and code to ensure maintainability and reproducibility
- Collaborate with business teams to translate requirements into effective technical solutions
- Bachelor’s or Master’s degree in Computer Science or a related field
- 3+ years of relevant experience with a Master’s degree, or 5+ years with a Bachelor’s degree
- Proven experience in developing and deploying GenAI-powered applications such as intelligent chatbots, AI copilots, and autonomous agents
- Strong understanding of Large Language Models (LLMs), transformer architectures (e.g., BERT, GPT, T5), and their applications in text generation, summarization, question answering, and code synthesis
- Strong understanding of Retrieval-Augmented Generation (RAG), embedding techniques, knowledge graphs, and fine-tuning/training of large language models (LLMs)
- Experience in natural language processing (NLP), prompt engineering, instruction tuning, context window optimization, advanced tokenization strategies, and leveraging pre-trained LLMs (via APIs or open-source models)
- Proficiency with LLM orchestration frameworks such as LangChain, LlamaIndex, and agentic/multi-agent orchestration tools like LangGraph, CrewAI, or similar
- Direct working experience in developing and implementing an interactive search platform Glean
- Proficiency in programming languages such as Python and Bash, as well as frameworks/tools like React and Streamlit. Experience with any copilot tools for coding such as Github copilot or Cursor
- Hands-on experience with vector databases such as FAISS, Pinecone, Weaviate, and Chroma for embedding storage and retrieval
- Familiarity with data preprocessing, augmentation, and visualization techniques
- Proven track record of contributing to GenAI projects from ideation through deployment, iteration, and evaluation of LLM performance
- Experience working with containerization and orchestration technologies like Docker, Kubernetes, and AWS ECS
- Hands-on expertise with key AWS services including VPC, IAM, MWAA (Managed Workflows for Apache Airflow), and ECS
- Familiarity with software development best practices including Git, testing, CI/CD pipelines, infrastructure as code (Terraform), automation, and MLOps for GenAI
- Strong commitment to engineering excellence through automation, innovation, and documentation
- One or more certifications such as Cloud, Solution Architect, Technical Architect, or GenAI-related certifications
- Proficiency in cloud platforms such as AWS and Azure
- Strong problem-solving skills and the ability to think creatively
- Strong collaboration skills in cross-functional teams (Product, Design, ML, Data Engineering)
- Ability to explain complex GenAI concepts to both technical and non-technical stakeholders