Description

You will design, build, and fine-tune NLP and LLM solutions for business use cases, including classification, summarization, and Q&A.

This role is on-site.

Responsibilities

• Develop efficient, well-documented Python code for training, inference, and evaluation pipelines.
• Build RAG applications using embeddings, vector databases, and prompt engineering techniques.
• Integrate LLM applications into services and APIs while ensuring performance, reliability, and scalability.
• Establish model evaluation, monitoring, and governance practices covering quality, safety, bias, and drift.
• Collaborate with data engineering and platform teams on data pipelines, deployments, and CI/CD.

Required Skills

• 6+ years of overall experience in software development, data analytics, data science, or ML engineering.
• 2+ years of hands-on experience with deep learning for NLP or GenAI.
• Strong Python proficiency, including writing production-quality, testable, and maintainable code.
• Experience with deep learning frameworks such as PyTorch or TensorFlow and Hugging Face Transformers.
• Solid understanding of deep learning architectures, tokenization, attention mechanisms, and fine-tuning approaches.
• Experience building rapid prototypes and APIs using FastAPI, Flask, or Streamlit.
• Proficiency with MLOps tools including Docker, Kubernetes, CI/CD, MLflow, and model registry.
• Strong software engineering fundamentals: Git, code reviews, unit/integration testing (pytest), and REST APIs.

Preferred Skills

• Experience with LLM orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel) or vector databases (FAISS, Pinecone, Weaviate).
• Experience deploying and scaling ML/LLM workloads on cloud platforms (Azure, GCP, or AWS).
• Familiarity with agentic architectures, multi-agent patterns, or healthcare domain knowledge in regulated environments.

Key Skills

Education

Bachelor's degree