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.