AI/ML Engineer (Generative AI & LLMs)

About Company:

Tudip Technologies Pvt. Ltd is a CMMI Level 5 extreme technology company. Careers at Tudip Technologies are not just jobs, but a promise of a bright and dynamic future. Tudip provides ample opportunities to grow within the company technically as well as a technocrat by promoting entrepreneurship. Tudip Technologies’ careers will enable you to help clients enhance and improve while you build your career. We are a place which defines Integrity, Innovation, and Serenity. Tudip provides you a better platform that transforms an individual into an experienced and immensely skilled professional through an ethical and vibrant business environment. We are here for effective client servicing, taking care of our employees’ needs, and creating a success story to remember.

Position Summary:

We are seeking a talented AI/ML Engineer to design, develop, and deploy AI-powered applications using Large Language Models (LLMs) and modern machine learning techniques. The ideal candidate will have strong experience in transformer-based models, LLM engineering, scalable backend systems, and cloud-based AI infrastructure.

You will play a key role in building intelligent platforms that leverage Generative AI, retrieval-augmented systems, and scalable microservices architectures to deliver impactful AI-driven solutions.

Please read the job criteria below and drop us an email at [email protected] OR create an account at our Recruitment Portal to get started.

Key Responsibilities

AI / ML Development:

• Design and build AI-powered applications using transformer-based Large Language Models (LLMs).
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for context-aware AI systems.
• Implement prompt engineering techniques to improve LLM accuracy and performance.
• Apply techniques to reduce hallucinations and improve model reliability.

LLM Engineering:

• Build end-to-end LLM pipelines using LangChain or LlamaIndex.
• Integrate vector databases for semantic search and contextual retrieval.
• Develop AI workflows involving multiple model interactions and tool integrations.
• Contribute to improving LLM evaluation and monitoring frameworks.

Backend & Platform Development:

• Develop scalable Python-based backend services for AI applications.
• Design and implement REST APIs for AI-driven systems.
• Build systems using microservices architecture and distributed systems principles.

Data Infrastructure & Scalability:

• Implement scalable pipelines using Redis, message queues (Pub/Sub or Kafka), and caching mechanisms.
• Architect backend systems capable of handling large-scale user traffic and AI workloads.
• Ensure system performance, reliability, and efficient resource utilization.

Cloud & Deployment:

• Deploy and manage AI systems on Google Cloud Platform (preferred) or AWS/Azure.
• Work with cloud-native infrastructure for model deployment, scaling, and monitoring.

Collaboration:

• Collaborate with product managers, data scientists, and engineering teams to translate business requirements into AI solutions.
• Participate in architecture discussions and technical decision-making.
• Continuously explore and adopt latest advancements in AI and Generative AI technologies.

Required Skills & Qualifications

Education:

Bachelor’s or Master’s degree in:

• Computer Science
• Artificial Intelligence.
• Machine Learning.
• Data Science.

or equivalent practical experience in AI/ML engineering.

Experience:

• 2+ years of experience working with AI/ML or LLM-based systems.

AI / Machine Learning:

• Strong understanding of Transformer architecture.
• Experience working with Large Language Models (LLMs).
• Knowledge of RNNs, LSTMs, and the evolution toward transformer-based architectures.
• Understanding of Reinforcement Learning from Human Feedback (RLHF).

LLM Engineering:

• Prompt engineering.
• Retrieval-Augmented Generation (RAG) pipelines.
• Experience with LangChain or LlamaIndex.
• Experience working with vector databases.

Backend & Systems:

• Python programming.
• Building REST APIs.
• Microservices architecture.
• Distributed systems design.

Data & Infrastructure:

• Redis.
• Message queues such as Google Pub/Sub or Kafka.
• Designing scalable backend systems.

Cloud Platforms:

• Google Cloud Platform (preferred).
• Experience with AWS or Azure also acceptable.

Good to Have

• Experience building AI developer tools or AI platforms.
• Knowledge of test automation frameworks.
• Experience with LLM evaluation frameworks.
• Familiarity with MLOps pipelines and model lifecycle management.