Ai - Ml Engineer

We are seeking an experienced AI-ML Engineer to join our team in Hyderabad, Telangana, India. This is a full-time, on-site position that requires a highly skilled individual to design, develop, and deploy machine learning models and AI solutions.

Role Overview:
We’re seeking an AI/ML Engineer to explore, evaluate, and optimize open-source models
for multimodal classification and annotation tasks. You’ll work across the full lifecycle –
from model exploration and fine-tuning to optimizing inference pipelines and integrating
with Meeami’s data platform.
This role is ideal for someone passionate about applied machine learning – eager to
experiment with emerging architectures, improve model performance, and bring AI
capabilities into production workflows.

Key Responsibilities:
• Research, evaluate, and benchmark open-source models for classification,
detection, and annotation tasks across text, audio, image, and video.
• Fine-tune and optimize models for accuracy, latency, and resource efficiency on
real-world datasets.
• Build and maintain inference pipelines and APIs (using FastAPI, TorchServe, or
Triton) for seamless integration with backend services.
• Collaborate with backend and data teams to design data flows for training, testing,
and evaluation.
• Perform exploratory analysis and visualization to understand dataset quality and
model behavior.
• Define and track evaluation metrics to continuously measure model performance
and reliability.

• Contribute to early training pipelines, experiment tracking, and data versioning
initiatives.

Qualifications:
• 3–6 years of experience in applied machine learning or AI engineering.
• Strong programming skills in Python, with hands-on experience in PyTorch or
TensorFlow.
• Familiarity with data preprocessing and augmentation for text, audio, image, or
video datasets.
• Experience running and profiling models for inference (GPU/CPU) using ONNX,
TorchScript, or TensorRT.
• Working knowledge of FastAPI or similar frameworks for serving ML models.
• Practical understanding of Git, CI/CD, Docker, and Linux environments.
• Comfort working in cloud environments (AWS/GCP/Azure) and collaborating in
agile, cross-functional teams.

Preferred Experience:
• Experience with audio processing, speech recognition, or computer vision models
(classification, segmentation, detection).
• Familiarity with annotation workflows and dataset QA.
• Understanding of model evaluation metrics (precision, recall, F1, mAP, AUC).
• Exposure to model optimization techniques (quantization, pruning, distillation).
• Experience with ML experiment tracking and dataset versioning tools (MLflow, DVC,
Weights & Biases).
• Bonus: Knowledge of transformer-based and multimodal architectures (Whisper,
CLIP, CLAP, LLaVA, SAM, etc.).