AI-ML Engineer

2 weeks ago

Hyderabad, Telangana, India Awign Expert Full time
AI-ML Engineer
Location: HYD, India

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.).
.

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