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NeoQuant Solutions Pvt Ltd · IT - Software Development

Senior Gen AI Engineer

· Job Posted March 06, 2026
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Job Description

We are seeking a highly skilled Senior Gen-AI & AI/ML Engineer to design, build, and deploy intelligent agentic systems that solve complex, real-world problems at enterprise scale. You will work on cutting-edge AI frameworks, multimodal pipelines, MCP-based infrastructures, and agent-driven workflows that combine autonomous reasoning with human-in-the-loop learning. This role is ideal for engineers who enjoy hands-on model development, production-grade deployments, and building advanced AI capabilities that directly impact business outcomes.

Job Responsibility

  • Design and deploy intelligent, agent-driven systems that autonomously solve complex business problems using advanced AI algorithms
  • Engineer collaborative multi-agent frameworks capable of coordinated reasoning and action for large-scale applications
  • Build and extend MCP-based infrastructure enabling secure, context-rich interactions between agents and external tools/APIs
  • Develop workflows that combine agent autonomy with human oversight, enabling continuous learning through feedback loops (e.g., RLHF, in-context correction)
  • Build, fine-tune, train, and evaluate ML and deep-learning models using frameworks such as PyTorch and TensorFlow
  • Work with multimodal data pipelines (text, images, structured data) for embedding generation, feature extraction, and downstream tasks
  • Integrate models into production systems via APIs, inference pipelines, and monitoring tools
  • Use Git, testing frameworks, and CI/CD processes to ensure high-quality, maintainable code
  • Document architectural decisions, trade-offs, and system behavior to support collaboration across teams
  • Stay updated with AI research trends and apply relevant advancements into product design

Requirements

  • 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
  • Experience: 5 Years to 9 Years

Nice 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
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