Senior Staff Engineer; Machine Learning

Company Description

We're Nagarro.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at a scale — across all devices and digital mediums, and our people exist everywhere in the world (17500+ experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues.

That is where you come in!

• 7.5+ years of hands‑on experience in Machine Learning / AI Engineering.
• Proven delivery experience in BFSI, especially in GenAI solutions.
• Strong proficiency in Python, PyTorch, Tensor Flow, Hugging Face.
• Deep understanding of LLM architecture, transformers, embeddings, tokenization.
• Experience with LLM fine‑tuning:
  LoRA, PEFT, prompt tuning.
• Experience building RAG systems with Pinecone, FAISS, Weaviate, Chroma.
• Expertise in Lang Chain, Llama Index, DSPy, Semantic Kernel.
• Experience with AWS, Azure, GCP and AI services like AWS Bedrock, Azure OpenAI, Vertex AI.
• Experience with Open Search / Elastic Search and vector search.
• Strong grounding in model evaluation, benchmarking, hallucination detection.
• Ability to design production‑ready ML systems.

• Design, develop, and deploy machine learning models for BFSI use cases such as credit scoring, fraud detection, churn prediction, and customer engagement.
• Build GenAI and NLP solutions using Python, PyTorch, Tensor Flow, Hugging Face, and Lang Chain.
• Develop LLLM‑based applications including fine‑tuning (LoRA, PEFT, prompt tuning) and RAG pipelines using vector databases (Pinecone, FAISS, Chroma, Weaviate).
• Implement MLOps pipelines using CI/CD, MLflow, Docker, Kubernetes.
• Build scalable REST/Graph
  
  QL APIs using FastAPI, Flask, or similar frameworks.
• Design and deploy conversational AI workflows using Dialogflow, Rasa, or Microsoft Bot Framework.
• Ensure strong model governance, compliance, explainability (SHAP, LIME), and data security practices.
• Conduct model evaluation with metrics for grounding, hallucination detection, and benchmarking.
• Collaborate with business and data teams to identify AI opportunities and define success metrics.
• Optimize ML and GenAI systems for performance, scalability, and cost.

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.