Senior AI/ML Engineer

Timble AI; Bengaluru; Hybrid; INR 2,500,000 - 4,000,000; Full time

Job summary

Timble Technologies Pvt. Ltd invites a hands-on Senior AI/ML Engineer to design, develop, and productionize high-throughput AI/ML and GenAI systems, delivering sub-second latency for enterprise products.

You will own the full lifecycle from data pipelines to model governance, collaborate across teams, and mentor junior engineers in a hybrid Gurugram setup.

Experience with LLMs, RAG, MLOps, and cloud platforms is required, along with strong Python, SQL, and software engineering fundamentals.

Qualifications

• 2+ years hands-on experience deploying ML/GenAI models in production.
• Education: B.Tech / M.Tech in CS/AI/Math, Tier-1 institutes preferred.
• Experience with LLMs, embeddings, RAG architectures, and LangChain/LlamaIndex/Hugging Face.
• Strong Python skills, SQL, and knowledge of MLOps.

Responsibilities

• Design, train, and deploy production-scale ML/Deep Learning and GenAI systems.
• Develop GenAI workflows with prompt engineering, RAG pipelines, and vector indexing.
• Build end-to-end data pipelines and production-grade Python code with SQL.
• Establish MLOps/LLMOps standards, monitoring, and governance; mentor juniors.

Skills

Python

ML/DL

NLP

LLMOps

LangChain

Education

B.Tech / M.Tech in CS/AI/Math

Tools

PyTorch

TensorFlow

LlamaIndex

Hugging Face

LangChain

Docker

Kubernetes

FastAPI

SQL

Job description

About Us

Timble Glance is a high-growth AI RegTech and B2B SaaS company catering to top-tier BFSI and enterprise clients. We build cutting-edge systems powering 30+ high-scale APIs for digital identity verification, fraud detection, document intelligence, and compliance automation.

Job Title

Senior AI/ML Engineer

Company

Timble Technologies Pvt. Ltd

Location

Gurugram (Hybrid)

Experience

2 TO 5 Years

Role Overview

We are looking for a hands‑on Senior AI/ML Engineer to design, develop, and productionize high‑throughput AI/ML and Generative AI systems. You will own the full lifecycle—from problem formulation and data pipelines to deep learning architectures, RAG systems, LLMOps, and model governance—delivering sub‑second latency and high reliability across our enterprise products.

Key Responsibilities

• Model Architecture & Deployment: Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (computer vision, document intelligence, OCR, NLP, fraud risk classification, and LLM applications).
• GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine‑tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.
• Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production‑grade Python (NumPy, Pandas) and advanced SQL.
• MLOps & Monitoring: Establish end‑to‑end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.
• Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.
• Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.

Required Qualifications & Skills

• Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier‑1 institutes (IIT, IIIT, NIT) strongly preferred.
• Experience: 2+ years of hands‑on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.
• GenAI & NLP Stack: Hands‑on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.
• Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.
• Software & Data Engineering: Expert‑level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.
• Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).

Preferred Qualifications

• Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.
• Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).
• Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

Skills

Amazon Web Services (AWS), Google Cloud Platform (GCP), Docker, API, Flask, Kubernetes, pytest, Object Oriented Programming (OOPs) and SQL