AI Engineer

AppZoy Technologies
Bengaluru, Karnataka

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Job details

Qualifications

B.E. Computer Engineering
TensorFlow
CI/CD
Torch
Image processing
Cloud infrastructure
Azure
Law
Computer Science
Software Engineering
Signal processing
Kubernetes
Computer vision
NumPy
Master's degree
Databases
Pandas
AWS
Docker
Bachelor's degree
Continuous integration
REST
Computer Engineering
OpenCV
APIs
JSON
Flask
AI
Python

Full job description

We’re seeking an AI Engineer with strong hands-on experience in computer vision and multimodal (vision + voice) systems—from data preparation and model training to scalable inference and production deployment. You’ll build and ship end-to-end AI solutions: curate datasets, fine-tune and evaluate models, optimize inference pipelines, and operationalize services on cloud infrastructure.

Job Requirements

Bachelor’s degree in Computer Engineering / Software Engineering / Electronics / Computer Science or equivalent (Master’s preferred).
0-3 years building production AI systems, with a focus on computer vision (PyTorch/TensorFlow).
Strong Python engineering skills; experience with REST/JSON APIs (FastAPI/Flask), and service design.
Hands-on with cloud deployment for AI (AWS/GCP/Azure), containers (Docker), orchestration (Kubernetes/Cloud Run/ECS), and GPUs.
Experience training and serving CV models (ResNet/EfficientNet, YOLO/RetinaNet, U-Net/nnU-Net, Vision Transformers), and familiarity with ASR/TTS pipelines (e.g., Whisper, torchaudio).
Practical knowledge of model optimization (ONNX/TensorRT, quantization), and data tooling (Pandas, NumPy, OpenCV, ffmpeg).
Proficiency with MLOps tooling: experiment tracking (MLflow/W&B), model registry, CI/CD, monitoring/logging (Prometheus/Grafana/Cloud Monitoring).
Strong grounding in evaluation methodology: metrics, ablation studies, error/bias analysis, and reproducible research practices.
Comfort with cloud storage, queues, and databases; experience integrating AI services into existing systems.
Computer vision: 2 years (Preferred)
Nice-to-Have:
- Experience with multimodal (vision + workflows; Hugging Face ecosystem.
- Knowledge of real-time/edge inference, TensorRT-LLM, vLLM.
- Background in signal processing or audio engineering; speaker diarization, voice cloning ethics.
- Experience in regulated domains with HITL workflows and documentation.

Main Job Duties

Model development and inference
- Implement, fine-tune, and evaluate CV models (classification, detection, segmentation, tracking) using PyTorch/TensorFlow.
- Build robust inference services (REST/JSON) with batching, streaming, and hardware acceleration (GPU, TensorRT/ONNX).
- Train and adapt voice/ASR/TTS models and integrate with vision pipelines (multimodal workflows).
Data and evaluation
- Own dataset lifecycle: collection, cleaning, labeling/specs, augmentation, and versioning.
- Define metrics and test sets; run offline/online evaluations (accuracy, latency, throughput, calibration) and error analysis.
- Develop data transformation and feature pipelines; maintain data quality checks and bias/fairness assessments.
Production engineering
- Containerize and deploy models to cloud (AWS/GCP/Azure) using Docker/Kubernetes/Cloud Run/ECS.
- Implement CI/CD, experiment tracking, model registry, A/B canaries, rollout/rollback strategies.
- Build monitoring for drift, performance, and cost; automate retraining or active learning loops.
Systems integration
- Design APIs and modules, integrate with upstream/downstream systems, and ensure reliable contracts and observability.
- Collaborate with product, design, and backend teams to turn requirements into measurable deliverables.
Performance and optimization
- Optimize training/inference (quantization, pruning, distillation, mixed precision); leverage ONNX/TensorRT/torch.compile.
- Profile and tune data loaders, GPU utilization, caching, and I/O.
Compliance and safety
- Implement data governance, privacy, and security best practices; maintain audit trails and documentation.
- Establish HITL workflows and guardrails for clinical or safety-critical contexts where applicable.

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Job Requirements

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