Senior Lead Engineer - Data Science

Airtel Digital; Gurugram District; Vor Ort; INR 4.200.000 - 6.400.000; Vollzeit

Zusammenfassung

Airtel Digital seeks a Senior Lead Engineer - Data Science to architect end-to-end AI systems, spanning traditional ML, Generative AI and Agentic AI, with experience in fine-tuning and computer vision.

You will lead system design and delivery, build scalable pipelines, oversee LLMOps, and collaborate with product teams to align business outcomes. Proficient in Python, PyTorch, and modern MLOps tools is expected.

Qualifikationen

• 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 ML, Generative AI and Agentic AI architectures.

Aufgaben

• Architect scalable pipelines for training/fine-tuning open source LLMs.
• Build 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.
• Build GenAI applications serving organisational use cases and reduce human dependencies.
• Model serving with vLLM, Triton; use of async inference and caching.
• Lead multidisciplinary teams; define technical roadmaps.
• Collaborate with Product and Research to align business outcomes.

Kenntnisse

Leadership

Machine Learning

Deep Learning

Generative AI / LLMs

Prompt engineering

Data preprocessing

Dataset creation

System design

Model evaluation

CV pipelines

Tools

Python

PyTorch

Hugging Face

vLLM

Triton

LangSmith

MLflow

BentoML

Weights & Biases

GitHub Actions

Terraform

ArgoCD

FAISS

Pinecone

Weaviate

Jobbeschreibung

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.
• Lead multidisciplinary teams; define technical roadmaps.
• Collaborate with Product and Research to align business outcomes.

Languages: Python, TypeScript, SQL, optionally Go/Rust

LLMOps: MLflow, LangSmith, BentoML, Weights & Biases

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.