Staff Engineer (Machine Learning)

Job description

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!

Job Description

REQUIREMENTS:

• 6+ years of hands-on experience in Machine Learning / AI Engineering.
• Proven delivery experience in BFSI, especially in GenAI solutions.
• Strong proficiency in Python, PyTorch, TensorFlow, 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 LangChain, LlamaIndex, DSPy, Semantic Kernel.
• Experience with AWS, Azure, GCP and AI services like AWS Bedrock, Azure OpenAI, Vertex AI.
• Experience with OpenSearch / ElasticSearch and vector search.
• Strong grounding in model evaluation, benchmarking, hallucination detection.
• Ability to design production-ready ML systems.

RESPONSIBILITIES:

• 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, TensorFlow, Hugging Face, and LangChain.
• 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/GraphQL 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.

Qualifications

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