What You'll Be Doing:
- Support build end-to-end GenAI applications such as chatbots, voicebots, Talk-to-Data systems, where you will help buiild: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and simple UI where needed.
- Implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness.
- Perform model selection, prompting strategies, and finetuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including guardrails, output evaluation and A/B testing.
- Integrating and optimizing LLMs interaction with external tools, APIs, and data sources in a standardized and scalable way by using Model Context Protocol (MCP) connectors
- Business understanding and translating problems into technical goals by defining tasks, auditing data feasibility, and highlighting issues.
- Supporting project delivery
What We’re Looking For:
- Familiarity with theory behind various deep learning concepts
- Experience with Machine Learning (ML), especially in Generative AI (LLM/LMM) with focus on Natural Language Processing (NLP) or multimodal models.
- Fluency in Python and object programing, working knowledge of SQL and vector database
- Tech stack in Azure or GCP cloud
- Knowledge of specific Deep Learning and GenAI libraries like: NumPy, PyTorch, HuggingFace, LangChain, LangGraph and GenAI APIs i.e. OpenAI/Gemini.
- Experience with microservice architectures
- Experience with code repositories and code assistants
- Strong business acumen
- Ability to come up with creative solutions to address customer problem
Nice to have:
- Experience in working with Databricks will be a plus
- Commercial experience proven by multiple successful projects in Generative AI, Natural Language Processing or Computer Vision will be a big plus