Senior Artificial Intelligence/Machine Learning Engineer

1 day ago

Gandhinagar, Gujarat, India BIG IT JOBS Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Description :

Key Responsibilities (What You'll Do) :

- You will be responsible for the full lifecycle of AI/ML services, from proof-of-concept to production deployment :

- LLM & RAG System Development : Design, implement, and optimize LLM Vector Database-based Retrieval-Augmented Generation (RAG) systems to power sophisticated, highly accurate, and domain-specific chat and knowledge retrieval applications.

- Agentic AI Implementation : Architect and develop Agentic AI frameworks and systems tailored for complex FinTech use cases (e.g., automated decision-making, intelligent workflow management).

- Model Training & Fine-Tuning : Conduct research and execute the training and fine-tuning of state-of-the-art transformer-based NLP models (LLMs) for critical classification, summarization, and generation tasks.

- Information Extraction Pipelines : Build and optimize high-throughput, accurate document classification and information extraction pipelines, ensuring data quality and system reliability.

- Computer Vision Integration : Apply Computer Vision (CV) and OCR techniques to tackle document- and image-based challenges, enhancing our ability to process complex financial documentation.

- Production Deployment : Deploy, monitor, and maintain scalable, low-latency ML services using modern MLOps principles and cloud environments (e.g., AWS, GCP, Azure).

- Code Quality : Uphold the highest standards for code quality, system performance, and semi-OCR methodology, ensuring robust and maintainable infrastructure.

Required Qualifications (What Were Looking For) :

Core Experience & Technical Skills :

- Experience : 3 years of hands-on experience as an ML Engineer, Data Scientist, or NLP Engineer, with a focus on building and deploying production-level systems.

- Programming : Expert-level proficiency in Python and its ecosystem, with a focus on performance-critical code.

- ML Frameworks : Strong command of major Machine Learning frameworks, including PyTorch (preferred), TensorFlow, and the Hugging Face ecosystem for transformer models.

- Vector Databases : Proven experience implementing solutions using any modern vector database (e.g., Qdrant, Pinecone, Chroma, Milvus).

- NLP/LLM Specialization : Demonstrated success in working with Large Language Models (LLMs), prompt engineering, and the principles of Agentic AI.

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