AI Engineer(9807)

Quarks Technosoft Pvt. Ltd.; Bengaluru; On-site; INR 3,500,000 - 5,200,000; Full time

Job summary

Quarks Technosoft Pvt. Ltd. is seeking an AI/ML Engineer in Bengaluru to build enterprise-grade chatbots and intelligent assistants using state-of-the-art LLM technologies. You will collaborate with Product, Data, and Platform teams to deliver reliable, explainable AI solutions at scale.

You will work on finance data and RAG-based architectures, focusing on hallucination mitigation and governance, while deploying production-ready AI systems with CI/CD and MLOps practices.

Qualifications

• 5–7 years of relevant experience in AI/ML engineering.
• Proven experience building enterprise-grade chatbots or intelligent assistants.
• Strong Python and cloud (AWS) capabilities required.

Responsibilities

• Build LLM-powered chatbots using OpenAI, RAG, tool calling, and agent frameworks (LangChain, LangGraph).
• Design Agent-to-Agent architectures for multi-step reasoning and autonomous workflows.
• Implement hallucination reduction techniques: grounding, re-ranking, citations, confidence scoring.
• Work with finance and enterprise datasets ensuring accuracy and governance.
• Deploy and monitor AI systems using cloud-native and MLOps practices.
• Implement CI/CD for AI pipelines and inference services.

Skills

Python

AI/ML

AWS

Tools

LangGraph

LangChain

MCP

A2A patterns

Job description

We are seeking an AI/ML Engineer to build enterprise-grade chatbots and intelligent assistants using state-of-the-art LLM technologies. The role focuses on finance data, RAG-based architectures, hallucination mitigation, and agentic AI systems deployed at scale.

You will work closely with Product, Data, and Platform teams to deliver reliable, explainable, andproduction-ready AI solutions.

Experience - 5 to 7 years relevant experience.

Primary Skills - Python, AI/ML, AWS

Secondary Skills - SQL

Responsibilities

• Build LLM-powered chatbots using OpenAI, RAG, tool calling, and agent frameworks (LangChain, LangGraph)
• Design Agent-to-Agent (A2A) architectures for multi-step reasoning and autonomous workflows
• Implement hallucination reduction techniques: grounding, re-ranking, citations, confidence scoring
• Work with finance and enterprise datasets ensuring accuracy and governance
• Deploy and monitor AI systems using cloud-native and MLOps practices
• Implement CI/CD for AI pipelines and inference services

Technologies & Skills

• Python, SQL
• Agentic AI: LangGraph, LangChain, MCP, Agent-to-Agent (A2A) patterns
• RAG & Search: embeddings, hybrid search, cross-encoders
• Vector Databases
• Evaluation & Observability: LangSmith, MLflow, Weights & Biases
• Data: Snowflake, DBT, structured & unstructured data pipelines
• Evaluation: prompt/version management, offline & online LLM evaluation