Senior Staff Engineer (Machine Learning) at Nagarro

, , India -

Full Time

Start Date

Immediate

Expiry Date

11 Mar, 26

Salary

0.0

Posted On

11 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, AI Engineering, Python, PyTorch, TensorFlow, Hugging Face, LLM Architecture, Transformers, Embeddings, Tokenization, MLOps, REST APIs, GraphQL, Conversational AI, Model Evaluation, Data Security

Industry

IT Services and IT Consulting

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: 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, 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.

Responsibilities

Design, develop, and deploy machine learning models for BFSI use cases such as credit scoring and fraud detection. Collaborate with business and data teams to identify AI opportunities and optimize ML systems for performance and scalability.