AI Engineer

Park+; Gurugram District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Park+ is seeking a talented ML engineer to design and deploy computer vision systems including detection, tracking, and video analysis.

You will build and train models from scratch, work with large language models, and explore autonomous reasoning and memory management for multi-step workflows. Collaboration with research to push performance boundaries is expected.

Qualifications

• Proficiency in Python and solid fundamentals in machine learning theory and practice
• Deep knowledge of linear algebra, probability theory, optimization methods, neural network design, deep learning frameworks, and model evaluation techniques
• Clear understanding of how different ML architectures work internally, not just their external APIs
• Hands-on experience with convolutional neural networks, object detection models, Transformer architectures, attention mechanisms, and large language model fine-tuning
• Familiarity with loss functions, end-to-end training pipelines, and inference optimization strategies

Responsibilities

• You will design and implement computer vision systems covering detection, optical character recognition, object tracking, and video analysis
• You will build machine learning models from scratch, train them on datasets, and optimize their performance for production use
• You will work with large language models, including retrieval-augmented generation pipelines, model fine-tuning, embedding techniques, and inference optimization for efficiency
• You will develop agentic AI systems that perform autonomous reasoning, planning, memory management, tool integration, and multi-step workflows
• You will create voice AI solutions including automatic speech recognition, text-to-speech synthesis, voice activity detection, and real-time voice agent systems
• You will contribute to ML research by reading and implementing recent papers, experimenting with novel architectures, and pushing model performance boundaries

Skills

Python

ML theory

Linear algebra

Probability theory

Optimization methods

Neural networks

Deep learning frameworks

CNNs

Object detection

Transformer architectures

Attention mechanisms

LLM fine-tuning

Inference optimization

Job description

Batch: 2023/2024/2025.

About the role

• You will design and implement computer vision systems covering detection, optical character recognition, object tracking, and video analysis
• You will build machine learning models from scratch, train them on datasets, and optimize their performance for production use
• You will work with large language models, including retrieval-augmented generation pipelines, model fine-tuning, embedding techniques, and inference optimization for efficiency
• You will develop agentic AI systems that perform autonomous reasoning, planning, memory management, tool integration, and multi-step workflows
• You will create voice AI solutions including automatic speech recognition, text-to-speech synthesis, voice activity detection, and real-time voice agent systems
• You will contribute to ML research by reading and implementing recent papers, experimenting with novel architectures, and pushing model performance boundaries

What we're looking for

• Proficiency in Python and solid fundamentals in machine learning theory and practice
• Deep knowledge of linear algebra, probability theory, optimization methods, neural network design, deep learning frameworks, and model evaluation techniques
• Clear understanding of how different ML architectures work internally, not just their external APIs
• Hands-on experience with convolutional neural networks, object detection models, Transformer architectures, attention mechanisms, and large language model fine-tuning
• Familiarity with loss functions, end-to-end training pipelines, and inference optimization strategies

About you

• You don't just apply existing models or call third-party APIs; you dig into how they work, experiment with modifications, identify failure modes, and improve them
• You can articulate your engineering decisions: why you chose a particular approach, what didn't work, how you debugged it, and what you learned
• You build things end-to-end rather than stopping at proof-of-concept