Senior Gen AI Engineer

NeoQuant - Neo Quant Solutions Pvt Ltd; India; Remote; INR 1,500,000 - 3,000,000; Full time

Job summary

NeoQuant - Neo Quant Solutions Pvt Ltd is seeking a Senior Gen-AI and AI/ML engineer to design, build, and deploy intelligent agentic systems at enterprise scale. You will work on multimodal pipelines and MCP-based infrastructure for secure agent-to-tool interactions.

The role requires 5–9 years in AI or software engineering, with at least 2 years in Generative AI, plus hands-on experience deploying RAG/LLM apps in production. Proficiency in LangChain and related tools is a plus.

Qualifications

• Bachelor's or Master's degree in CS, AI, engineering or related field.
• 5 to 9 years in AI or software engineering, with at least 2 years in Generative AI.
• Proven experience deploying RAG systems or LLM applications in production.

Responsibilities

• Design and deploy agent-driven systems that autonomously solve complex business problems.
• Engineer collaborative multi-agent frameworks for coordinated reasoning and action.
• Build and extend MCP-based infrastructure for secure agent-to-tool/API interactions.
• Develop human-in-the-loop workflows combining agent autonomy with human oversight (for example RLHF, in-context correction).
• Build, fine-tune, train and evaluate ML and deep-learning models using PyTorch and TensorFlow.
• Work with multimodal data pipelines (text, images, structured data) for embedding generation and feature extraction.
• Integrate models into production via APIs, inference pipelines and monitoring toolsUse Git, testing frameworks and CI/CD for maintainable code.
• Document architectural decisions and stay current with AI research trends.

Skills

Python

Agentic frameworks

ML fundamentals

Multimodal data pipelines

Production deployment

LLMOps

Education

Bachelor's/Master's in CS/AI/Engineering

Tools

LangChain

LangGraph

AutoGen

MCP

Google ADK

CI/CD

Job description

Looking for a Senior Gen-AI and AI/ML engineer to design, build and deploy intelligent agentic systems, multimodal pipelines and MCP-based infrastructure that solve complex, real-world problems at enterprise scale.

Responsibilities

• Design and deploy agent-driven systems that autonomously solve complex business problems
• Engineer collaborative multi-agent frameworks for coordinated reasoning and action
• Build and extend MCP-based infrastructure for secure agent-to-tool/API interactions
• Develop human-in-the-loop workflows combining agent autonomy with human oversight (for example RLHF, in-context correction)
• Build, fine-tune, train and evaluate ML and deep-learning models using PyTorch and TensorFlow
• Work with multimodal data pipelines (text, images, structured data) for embedding generation and feature extraction
• Integrate models into production via APIs, inference pipelines and monitoring toolsUse Git, testing frameworks and CI/CD for maintainable code
• Document architectural decisions and stay current with AI research trends

Required Skills

• Strong fluency in Python and Agentic frameworks.
• Solid understanding of ML fundamentals: optimization, representation learning, evaluation metrics, supervised/unsupervised/generative modeling.
• Hands-on experience with multimodal datasets and feature pipelines.
• Experience deploying ML models to production, including inference optimization and monitoring.
• Familiarity with LLMOps/MLOps concepts: versioning, reproducibility, observability, governance.

Good to Have

• Experience designing goal-oriented agentic systems and multi-agent coordination workflows.
• Proficiency with LangChain, LangGraph, AutoGen, Google ADK or similar agent orchestration frameworks.
• Knowledge of secure tool/agent communication protocols such as MCP.
• Exposure to reinforcement learning from human feedback (RLHF) and reward modeling.
• Cloud experience

Qualifications

• Bachelor's or Master's in Computer Science, AI, Engineering or related field
• 5 to 9 years in AI or software engineering, with at least 2 years in Generative AI
• Proven experience deploying RAG systems or LLM applications in production