Senior AI/ML Engineer

timbletech; Dadri, Gurugram District, Delhi; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Timbletech is seeking 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.

Location includes Delhi, Gurugram, Noida, Kanpur, Lucknow, Bangalore.

Qualifications

• 2+ years hands-on experience deploying ML/DL or GenAI models in production.
• Experience with LLMs, embeddings, RAG architectures, and frameworks like LangChain or HuggingFace.
• Proficiency in Python, PyTorch/TensorFlow, and SQL.
• Experience with cloud platforms (AWS/GCP), Docker, and API frameworks (FastAPI/Flask).
• Strong understanding of model governance, security, and responsible AI principles.

Responsibilities

• Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (CV, NLP, fraud risk).
• Develop GenAI workflows: prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing, safety guardrails.
• Build data pipelines and production-grade code in Python (NumPy, Pandas) and SQL.
• Establish MLOps/LLMOps standards, governance, monitoring, and latency optimizations.
• Mentor junior engineers and translate business requirements into technical roadmaps.

Skills

Python

PyTorch

LLMs

NLP/CV

SQL

Git

pytest

Education

B.Tech / M.Tech in Computer Science, AI/ML, Mathematics or related field

Tools

LangChain

LlamaIndex

HuggingFace

Docker

Kubernetes

FastAPI/Flask

Pinecone

Milvus

Chroma

NumPy

Pandas

Airflow

Job description

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

Location - Delhi, Gurugram, Noida, Kanpur, Lucknow, Bangalore.