AI/ML Engineer

Nucleus Software Exports; Dadri; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

Nucleus Software Exports is seeking an experienced AIML Architect to lead end-to-end ML initiatives for lending and transaction banking. You will design, implement and deploy DL and NLP solutions, integrating them with existing enterprise systems.

The role requires 6+ years in AI/ML with strong Java and Python skills, plus hands-on experience with TensorFlow, PyTorch, and Keras. Prior fintech exposure is highly valued.

Qualifications

• 6+ years in AIML with 2+ years in enterprise software.
• Expertise in deep learning frameworks (TensorFlow, PyTorch, Keras).
• Strong proficiency in Java and Python.
• In-depth NLP experience with LLMs (GPT models, BERT).
• Experience with vision-based algorithms for finance use cases.
• Knowledge of lending and transaction banking processes.
• Proven track record of scalable AI in enterprise environments.
• Strong problem-solving skills and adaptability to evolving business needs.

Responsibilities

• Lead design, development, and deployment of AIML solutions for lending and transaction banking.
• Architect and implement DL models using TensorFlow, PyTorch, and Keras.
• Drive NLP/LLM development for document processing and customer profiling.
• Implement vision-based algorithms for document verification and KYC.
• Collaborate with stakeholders to map business needs to AI features.
• Ensure ML lifecycle practices: training, tuning, deployment in enterprise environments.
• Guide scalability and performance of AI in financial software.

Skills

Java

Python

NLP

Problem-solving

Adaptability

Education

BE/BTech from Tier 1 Institute

Tools

TensorFlow

PyTorch

Keras

MLOps

Job description

Key Responsibilities

• Lead the design, development, and deployment of AIML solutions for the lending and transaction banking businesses, focusing on high-impact areas like credit scoring, risk assessment, payment processing, and fraud detection.
• Architect and implement Deep Learning models using frameworks such as TensorFlow, PyTorch, and other modern ML libraries.
• Drive the development and optimization of NLP models and Large Language Models (LLMs) for applications like automated document processing, customer profiling, and customer interaction.
• Implement vision-based algorithms for document verification, KYC (Know Your Customer) processes, and transaction monitoring.
• Collaborate with business stakeholders to understand the business processes, define AI-driven features, and ensure smooth integration of machine learning models into the existing technology stack.
• Ensure best practices in machine learning lifecycle management, including model training, tuning, and deployment in enterprise environments.
• Guide in managing the scalability, performance, and robustness of AI solutions, ensuring they meet the needs of enterprise software systems in transaction banking and lending.

Requirements

• 6+ years of experience in developing and deploying AIML solutions, with at least 2 years of experience in enterprise software development.
• Expertise in Deep Learning frameworks like TensorFlow, PyTorch, and Keras.
• Strong proficiency in Java, Python, and related programming languages.
• In-depth understanding of Natural Language Processing (NLP), with experience working on LLMs (e.g., GPT models, BERT).
• Experience with vision-based algorithms and their application in real-world financial problems.
• Knowledge of business processes in the lending domain and transaction banking.
• Proven track record of building scalable and robust AI solutions in enterprise environments.
• Strong problem-solving skills, with the ability to adapt AIML approaches to rapidly evolving business needs.

Preferred Qualifications

• BE/BTech from Tier 1 Institute.
• Previous experience in the lending, transaction banking, or fintech industries.
• Familiarity with enterprise software architecture and integration patterns, especially in the context of financial services.
• Hands-on experience with MLOps practices and tools for deploying and maintaining machine learning models in production.