Machine Learning Engineer

Infinite Locus; Gurugram District; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Infinite Locus in Gurgaon is seeking an Applied AI Engineer to build and deploy production-grade AI systems spanning forecasting, data intelligence, and LLM-powered automation. You will design agentic AI systems, data pipelines, and predictive models that power business decisions.

The role emphasizes software engineering rigor, hands-on ML development, and deploying AI systems in production using Docker, Kubernetes, and cloud platforms.

Qualifications

• Strong experience with Python and ML fundamentals.
• Experience with ML frameworks and production-grade model deployment.
• Familiarity with time-series forecasting models and data pipelines.

Responsibilities

• Build Production AI Systems across forecasting, data intelligence, and LLM-powered automation.
• Design and deploy LLM-powered agent systems using LangGraph, AutoGen, or LangChain.
• Develop AI pipelines integrating structured data, APIs, and external sources; implement RAG architectures.

Skills

Python

PyTorch

Scikit-learn

LightGBM / XGBoost

Time-series forecasting

Docker

Kubernetes

FastAPI

LangChain

LangGraph

AutoGen

RAG architectures

Tools

Docker

Kubernetes

FastAPI

AWS

GCP

Azure

LangChain

LangGraph

AutoGen

LLaMA

Mistral

Mixtral

Pinecone

Weaviate

FAISS

Job description

Applied AI / LLM Systems Engineer

Location: Gurgaon

Team: AI/ML

Role Overview

We are looking for a highly skilled Applied AI Engineer to build and deploy production-grade AI systems across forecasting, data intelligence, and LLM‑powered automation. This role focuses on designing agentic AI systems, data pipelines, and predictive models that directly power business decision‑making. You will work on building systems such as price intelligence engines, automated data pipelines, forecasting models, and LLM‑powered decision agents. The ideal candidate combines strong ML fundamentals, software engineering rigor, and experience deploying AI systems in production.

Key Responsibilities

• Build Production AI Systems
• Design and deploy LLM-powered agent systems using frameworks like LangGraph, AutoGen, or LangChain
• Build AI pipelines integrating structured data, APIs, and external sources
• Implement RAG architectures for knowledge‑driven applications

ML Infrastructure & Deployment

• Deploy models using:
• Docker
• Kubernetes
• FastAPI
• AWS / GCP
• Build scalable ML pipelines
• Implement model monitoring and performance tracking

AI Product Development

• Convert business problems into AI‑driven solutions
• Build decision‑support tools powered by ML models
• Work closely with leadership to implement AI‑first workflows

Required SkillsCore AI / ML

• Strong experience with Python
• Machine learning frameworks:
• PyTorch
• Scikit‑learn
• LightGBM / XGBoost
• Experience building time‑series forecasting models

LLM Systems

• Experience building applications with:
• LangChain
• LangGraph
• AutoGen
• Experience with RAG architectures
• Familiarity with open‑source models such as:
• LLaMA
• Mistral
• Mixtral

Data Engineering

• SQL and data modeling
• ETL pipeline development
• Web scraping and API integrations

Infrastructure

• Docker
• Cloud platforms (AWS/GCP/Azure)
• CI/CD pipelines

Good to Have

• Experience with agentic AI systems
• Vector databases (Pinecone, Weaviate, FAISS)
• Experience with price optimization systems
• Experience building data products or internal AI tools

Ideal Candidate Profile

• 35 years of experience in ML/AI engineering
• Strong programming and system design skills
• Experience shipping AI systems to production
• Ability to translate business problems into AI solutions