Develop, train, and fine-tune large language models and generative architectures (LLMs, VAEs, Transformers, GANs)
Integrate models with applications using LangChain, LlamaIndex, RAG, and other frameworks
Design LLM-based agents for specific use cases: summarization, classification, scoring, Q&A, translation
Build prompt templates and semantic memory flows using vector databases like Pinecone or FAISS
Collaborate with backend and data teams to ingest data from PDFs, APIs, structured databases, and JSON files
Benchmark model outputs and run experiments to optimize cost, performance, and quality
Stay on top of AI research and rapidly implement useful techniques in production environments
Write clear, modular, reusable code with documentation and test coverage
Troubleshoot model-related deployment or inference issues
Technical Skills Required :
Strong Python programming skills
Experience with Transformers, Hugging Face, OpenAI/Anthropic APIs, Med-GEMMA, or similar foundation models
Experience with agentic frameworks: LangChain, LlamaIndex, LangGraph, semantic RAG
Familiarity with Vector database experience (Pinecone, FAISS, Weaviate, or similar)
Comfortable with prompt engineering, few-shot learning, fine-tuning basics
Ability to process and clean unstructured data (PDFs, notes, research papers, etc.)
Understanding of NLP metrics and model evaluation techniques
Bonus: experience with biomedical or clinical data (PubMed, ClinicalTrials.gov, etc.)
Bonus: experience with deploying models via FastAPI, Docker, or Streamlit
Personal Attributes :
Curiosity and willingness to learn new models and tools quickly
Attention to detail and commitment to quality
Ownership mindset—you care about the outcome, not just the code
Ability to work independently and push through ambiguity
Passion for building usable AI, not just research prototypes
Strong communication and collaboration skills across tech and domain teams
Bonus Qualities :
You’ve built or deployed LLMs into production
You’ve worked with healthcare or life sciences data
You can show personal projects, GitHub repos, or AI experiments that demonstrate your passion
Interview Process:
1 Technical round with Head of Engineering
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