Applied AI/ML Engineer

Location: Bengaluru, India

Function: (DEAI HV) Engineering

Requisition ID: R0126832

Position Overview

We are looking for an experienced Data Scientist / ML Engineer with deep expertise in applied machine learning and natural language processing (NLP), along with strong exposure to modern MLOps practices. The ideal candidate combines solid theoretical foundations with hands-on experience in building, deploying, and maintaining large-scale ML systems, including LLM-based applications, in cloud-native and Kubernetes-based environments.

What You Will Do

• Translate complex business and product requirements into scalable AI/ML solutions using classical ML, deep learning, and GenAI techniques
• Design, develop, fine-tune, and evaluate models for NLP tasks such as information extraction, classification, entity recognition, and semantic understanding
• Build and productionize end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring
• Implement LLM-based solutions using prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning approaches
• Deploy and manage training and inference workloads on Kubernetes-based platforms (e.g., Kubeflow, KServe, Ray, or similar)
• Develop scalable APIs and microservices for model inference with performance, latency, and cost considerations
• Establish continuous training, evaluation, and feedback loops (CI/CD/CT pipelines) for model improvement
• Monitor model performance, data drift, and system health in production, and implement automated retraining strategies
• Collaborate closely with data engineering, platform, and product teams to ensure seamless integration into production systems
• Ensure compliance with data privacy, security, and governance standards throughout the ML lifecycle
  
  

What You Will Need

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related field, with 6–10 years of industry experience delivering ML solutions in production
• Strong hands-on experience in applying statistical methods, classical ML, and deep learning to solve real-world problems
• Proven experience building and deploying NLP systems, particularly for information extraction, classification, and sensitive data detection
• Solid understanding of evaluation methodologies and metrics for NLP and ML systems (e.g., precision/recall, F1, ROC-AUC, BLEU, etc.)
• Practical experience with LLMs, including prompt engineering, fine-tuning, embeddings, and retrieval-based systems
• Strong understanding of data privacy regulations (e.g., GDPR, HIPAA) and secure ML practices
• Experience working in cross-functional teams to deliver production-grade systems with continuous feedback and iteration
• Strong problem-solving skills and ability to balance research with engineering pragmatism
  
  

Technical Skills

Core ML & NLP

• Proficiency in Python and ML ecosystem
• Strong experience with ML/DL frameworks: PyTorch, TensorFlow, Scikit-learn
• Experience with NLP libraries: spaCy, NLTK, Hugging Face Transformers
• Familiarity with LLM tooling: LangChain, LlamaIndex, OpenAI APIs, vector databases (FAISS, Pinecone, Weaviate), Langgraph
  
  

MLOps & Productionization

• Hands-on experience with Kubernetes for ML workloads (training and inference)
• Experience with Kubeflow, MLflow, KServe, Ray, Airflow, or similar orchestration tools
• Strong understanding of CI/CD pipelines for ML (CI/CD/CT)
• Experience with model serving frameworks (FastAPI, TorchServe, Triton, etc.)
• Experience with experiment tracking, model versioning, and reproducibility
  
  

Data Engineering & Systems

• Experience with distributed data processing tools (e.g., Spark, Dask)
• Familiarity with data pipelines and feature stores.
  
  

Nice to Have

• Experience with RAG architectures, Knowledge-graphs and GraphRAG, and knowledge-grounded LLMs
• Exposure to GPU/accelerator-based training and optimization
• Familiarity with observability tools (Prometheus, Grafana) for ML systems
• Experience with security, privacy-preserving ML, or federated learning