AI/ML Engineer with Knowledge of Front End

Visioapps Technology; Gurugram District; On-site; INR 1,200,000 - 2,600,000; Full time

Job summary

Visioapps Technology in India is seeking an AI/ML Engineer to design, train, fine-tune, and optimize agentic AI systems with autonomous reasoning, planning, and action execution. You will collaborate with product, platform, and engineering teams to deploy scalable and secure AI models.

Responsibilities include building multi-agent architectures, integrating LLM-based agents with tools and APIs, and improving latency, memory, and cost efficiency through quantization, pruning, LoRA, and

Qualifications

• Strong understanding of agentic AI concepts.
• Experience with LLMs and reinforcement learning.
• Proficiency in data labeling and dataset preparation.

Responsibilities

• Design agentic AI architectures with multi-agent systems.
• Train and fine-tune foundation models for production use.
• Integrate agents with external tools and APIs and ensure secure deployment.
• Optimize models for latency, memory, and cost efficiency using quantization, pruning, LoRA/QLoRA.
• Conduct comprehensive evaluation, benchmarking, and bias testing.

Skills

Agentic AI

Multi-agent systems

LLM integration

Autonomous agents

Reinforcement learning

Model optimization

Latency optimization

Tools

APIs & SDKs

Job description

Role Overview

The AI/ML Engineer will be responsible for designing, training, fine-tuning, and optimizing AI/ML models, with a strong focus on Agentic AI systems capable of autonomous reasoning, planning, and action execution. The role involves working closely with product, platform, and engineering teams to deploy scalable, efficient, and secure AI models in production environments.

Key Roles & Responsibilities

01: Agentic AI Model Development

• Design and implement agentic AI architectures (multi-agent systems, tool-using agents and planners)


• Develop autonomous agents capable of decision-making, reasoning, and task execution


• Integrate LLM-based agents with external tools, APIs, workflows, and SDKs

02: Model Training & Fine-Tuning

• Fine-tune LLMs and foundation models using techniques such as:


• Supervised Fine-Tuning (SFT)


• Reinforcement Learning from Human Feedback (RLHF)


• Preference Optimization (DPO, PPO)


• Train domain‑specific models using structured and unstructured datasets


• Prepare, clean, label, and validate training datasets

03: Model Optimization

• Optimize models for latency, memory, and cost efficiency


• Apply techniques such as:


• Quantization


• Pruning


• Knowledge distillation


• Low‑Rank Adaptation (LoRA / QLoRA)


• Enable efficient inference on cloud, edge, or hybrid environments

04: Evaluation & Validation

• Define evaluation metrics for:


• Accuracy


• Reasoning quality


• Hallucination reduction


• Task success rate


• Perform benchmarking and A/B testing


• Conduct bias, robustness, and adversarial testing