airtel
Website:
airtel.com
Job details:
Role Overview:
We are seeking for Senior Lead Engineer - Data Science for building end-to-end AI systems, spanning traditional machine learning, Generative AI (LLMs, Diffusion Models), and Agentic AI architectures (e.g., LangGraph, CrewAI, AutoGen) along with good experience with fine tuning, computer vision frameworks such as OpenCV. You will drive system design, architecture, and delivery of intelligent services embedded into production systems.
Key Responsibilities:
System Architecture & Engineering Oversight
- Architect scalable pipelines for training/fine-tuning open source LLMs
- Building framework for reducing Agent hallucinations, low latency and reliable output
- RAG pipelines with semantic chunking, hybrid retrieval, and context injection.
- Implement vector indexing (FAISS, Pinecone, Weaviate) with hybrid search.
- Own LLMOps stack: prompt versioning, evaluation, monitoring, and drift detection.
- Develop and optimize deep learning models for computer vision tasks such as Image classification/Face Authentication
Technical Delivery & Innovation
- Build GenAI applications serving organisational use cases and reduce human dependencies
- Model serving with vLLM, Triton; use of async inference and caching.
Team Leadership & Execution
- Lead multidisciplinary teams; define technical roadmaps.
- Ensure CI/CD, modular ML design, containerization.
- Collaborate with Product and Research to align business outcomes.
Languages: Python, TypeScript, SQL, optionally Go/Rust
ML/AI: PyTorch, HuggingFace, LangChain/LangGraph, OpenAI APIs, llama.cpp, OpenCV
LLMOps: MLflow, LangSmith, BentoML, Weights & Biases
Infra: Docker, Kubernetes, Ray, AWS/GCP
Vector DBs: FAISS, Qdrant, Weaviate, Elasticsearch
DevOps: GitHub Actions, Terraform, ArgoCD
Requirements:
- 7+ years in ML/AI; 2+ years in leadership.
- Production experience with GenAI and agent-based systems.
- Deep understanding of LLMs, embeddings, attention, prompt engineering.
- Experience with scalable AI infra and high-throughput serving.
- Strong experience in building CV pipelines for classification, detection and face recognition
- Building end-to-end AI systems, spanning traditional machine learning, Generative AI (LLMs,Diffusion Models), and Agentic AI architectures (e.g., LangGraph, CrewAI, AutoGen)
- Strong fundamentals in Machine Learning, Deep Learning and Generative AI
- Hands-on experience in data cleaning, normalization, preprocessing and feature engineering
- Experience in dataset creation, curation, labeling and quality validation
- Strong understanding of Transformers, attention, embeddings and tokenization
- Hands-on experience in training and fine-tuning SLMs/LLMs
- Experience with SFT, LoRA/QLoRA, PEFT and instruction tuning
- Strong hands-on with Python, PyTorch and Hugging Face
- Experience in model evaluation, benchmarking, error analysis and hyperparameter tuning
- Understanding of model compression, quantization, distillation and inference optimization
- Ability to take models end-to-end from data preparation to training, evaluation and production deployment.
- OSS contributions to GenAI/Agentic frameworks.
- Industry-specific RAG experience (legal, finance, healthcare).
- Knowledge of vector compression, neuro-symbolic reasoning.
Why Join Us:
- Join a high-caliber team pushing boundaries in AI/LLMs.
- Shape agentic and generative AI product roadmaps.
Click on Apply to know more.
