Ai Ml Engineer

Job Title:

object detection, face detection, AI-based object measurement

Key Responsibilities

• Develop and optimize

  computer vision models

  for:
• Object Detection
• Face Detection & Recognition
• AI-based Object Measurement (2D / 3D)
• Train, fine-tune, and evaluate

  deep learning models

  using industry-standard algorithms.
• Perform

  model architecture customization and fine-tuning

  for performance and accuracy.
• Build and integrate

  LLM-based workflows

  using

  LangChain and LangGraph

  .
• Design and implement

  AI agents

  for task automation, reasoning, and decision-making.
• Work with

  agentic frameworks and platforms

  to build multi-step AI pipelines.
• Prepare and manage datasets including

  annotation, augmentation, and preprocessing

  .
• Optimize models for

  real-time inference, edge deployment, and scalability

  .
• Collaborate with product, backend, and cloud teams for production deployment.
• Conduct experiments, analyze results, and document AI solutions.

Required Skills & Qualifications

• Bachelors degree in

  Computer Science, AI, ML, Data Science, or related field

  .

• ~1 year of hands-on experience

  in AI/ML development.
• Strong programming skills in

  Python

  .
• Experience with

  Deep Learning frameworks

  :
• PyTorch
• TensorFlow / Keras
• Practical experience with

  Object Detection models

  :
• YOLO (v5/v8)
• SSD, Faster R-CNN
• Knowledge of

  Face Detection and Recognition pipelines

  .
• Strong understanding of

  OpenCV and image processing techniques

  .
• Hands-on exposure to

  object measurement using AI

  , including:
• Camera calibration
• Pixel-to-real-world scaling
• Perspective correction

Model Training & Architecture Knowledge (Must-Have)

• Supervised and semi-supervised learning
• Training algorithms:
• SGD, Adam, RMSProp
• Loss functions and evaluation metrics:
• Cross-entropy, MSE
• IoU, mAP, Precision / Recall
• Model fine-tuning techniques:
• Transfer learning
• Layer freezing / unfreezing
• Hyperparameter tuning
• Handling overfitting and underfitting
• Model optimization for inference speed and memory

Generative AI & Agentic Systems (Required / Preferred)

• Working knowledge of

  Large Language Models (LLMs)

  .
• Experience or exposure to:

• LangChain

  (chains, tools, memory, retrievers)

• LangGraph

  (stateful, multi-step workflows)
• Building

  AI agents

  for:
• Task automation
• Tool calling
• Reasoning workflows
• Familiarity with

  agentic platforms and orchestration concepts

  .
• Understanding of prompt engineering and context management.
• Integration of LLMs with external tools, APIs, and databases.

  Role & responsibilities