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

Applied AI/ML Engineer