Antino Labs - Senior AI ML Engineer - LLM Applications

Antino; Gurugram District; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Antino is seeking a highly skilled Machine Learning Engineer (GenAI focus) to design, develop, and maintain scalable AI-powered applications using Python and modern ML frameworks. You will work on end-to-end ML pipelines, GenAI systems, and deployment across multiple platforms.

The role requires strong software engineering fundamentals, experience with LLMs, RAG pipelines, and MLOps practices to monitor, optimize, and guardrail AI systems in production.

Qualifications

• Proven experience in building ML/GenAI systems and end-to-end pipelines.

Responsibilities

• Write clean, Python-based code following OOP principles.

Skills

Python

OOP

ML fundamentals

GenAI

LLM systems

APIs & microservices

MLOps

Data pipelines

Prompt engineering

Model deployment

Tools

PyTorch

TensorFlow

Scikit-learn

FAISS

Pinecone

Weaviate

LangChain

LlamaIndex

LLaMA

Mistral

GPT

Qwen

LoRA

QLoRA

Job description

Job Description:

About Antino

With the intention and conviction of emerging as an unparalleled IT Digital Transformation Services platform, we at Antino Labs are known for providing impeccable software services using cutting edge technology across the globe. Without ever compromising with the quality of our output and bringing talent and diligence on a common platform, we have been noticed for our efficiency and reliability.

Summary

With dynamic exposure to the industry, we believe in refining and redefining our standard according to the changes in the market's requirement. Our multiple years of experience in the industry has enabled us to register our global presence. Presently, our branch offices are in Bangalore, UK, Dubai, Canada, and the US.

In the coming years, we envisage more expansion to emerge as a global IT Service Summary :

We are seeking a highly skilled and versatile Machine Learning Engineer (with GenAI expertise) who combines strong software engineering fundamentals with deep experience in machine learning and modern Generative AI systems. The ideal candidate will design, develop, and maintain scalable AI-powered applications using Python, with a strong emphasis on Object-Oriented Programming principles.

You will play a key role in building end-to-end AI systems, including LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and agent-based workflows, alongside traditional ML models. This role demands strong analytical thinking, coding expertise, architectural design skills, and a deep understanding of the full ML and GenAI lifecycle from data processing and model development to deployment, monitoring, and optimization.

Key Responsibilities

• Core ML & Engineering :
• Write clean, efficient, and well-documented Python code following OOP principles (encapsulation, inheritance, polymorphism, abstraction).
• Build and manage end-to-end ML pipelines : data ingestion, preprocessing, model training, evaluation, and deployment.
• Develop scalable ML systems using frameworks like PyTorch, TensorFlow, and Scikit-learn.
• Generative AI (GenAI) & LLM Systems :
• Design and implement LLM-based applications (chatbots, copilots, automation tools).
• Build and optimize RAG pipelines using vector databases (e.g., FAISS, Pinecone, Weaviate).
• Develop agentic workflows using frameworks like LangChain, LlamaIndex, or similar.
• Implement prompt engineering, structured output generation, and tool/function calling.
• Fine-tune or optimize LLMs using techniques like LoRA, QLoRA, or instruction tuning.
• Work with open-source and proprietary LLMs (e.g., LLaMA, Mistral, GPT, Qwen).
• Software Design & Architecture :
• Design modular, scalable, and maintainable ML and GenAI systems.
• Build APIs and microservices for model serving and GenAI applications.
• Contribute to architectural decisions for AI platforms and products.
• Data Engineering for AI :
• Build data pipelines for feature engineering, transformation, and dataset versioning.
• Manage structured and unstructured data (documents, embeddings, logs).
• MLOps & LLMOps :
• Implement CI/CD pipelines for ML and GenAI systems.
• Manage model and prompt versioning, experiment tracking, and reproducibility.
• Monitor systems for performance, drift, hallucinations, latency, and cost.
• Implement guardrails, evaluation frameworks, and feedback loops for LLMs.
• Performance & Scalability :
• Optimize inference latency and cost for ML and LLM systems.
• Ensure scalability under production workloads (batch + real-time).
• Documentation :
• Create clear documentation for ML models, GenAI pipelines, APIs, and workflows.
  

(ref:hirist.tech)