Machine Learning Engineer

Lahore - Pakistan

Monthly Salary: Not Disclosed

Posted on: 23 hours ago

Vacancies: 1 Vacancy

Job Summary

Description

Devsinc is hiring a skilled AI & ML Engineer with more than 2 years of professional experience in building and fine-tuning Generative AI models (LLMs Diffusion Models) Vision-Language Models (VLMs) and both classical and deep learning systems developing solutions from scratch and taking them end-to-end into production.

This role combines modeling and MLOps expertise involving end-to-end ownership from model training and fine-tuning to optimization deployment and serving. Youll work on diverse high-impact projects such as Generative AI applications Stable Diffusion OCR theft detection and recommendation systems designing optimizing and serving custom models for real-world production use.

Key Responsibilities:

Develop production inference stacks: Convert and optimize models (Torch ONNX TensorRT) quantize/prune profile FLOPs and latency and deliver low-latency GPU inference with minimal accuracy loss.
Build robust model-serving infrastructure: Implement FastAPI/gRPC inference services token or frame-level streaming model versioning and routing autoscaling rollbacks and A/B testing.
Create Computer Vision solutions from scratch: Design pipelines for object detection theft detection OCR (document parsing structured extraction) and surveillance analytics; fine-tune Hugging Face pretrained models when beneficial.
Fine-tune Stable Diffusion and other generative models for brand- or style-consistent image generation and downstream vision tasks.
Train and fine-tune Vision-Language Models (VLMs) for multimodal tasks (captioning VQA multimodal retrieval) using both from-scratch and transfer-learning approaches.
Design and adapt LLM-based Generative AI systems for conversational agents summarization RAG pipelines and domain-specific fine-tuning.
Implement MLOps / LLMops / AIOps practices: Automate CI/CD for training and deployment manage datasets and experiments maintain model registries and monitor latency drift and performance with alerting and retraining pipelines.
Develop data acquisition & ingestion pipelines: Build compliant scrapers collectors and scalable ingestion systems with proxy rotation and rate-limit handling.
Integrate third-party models and APIs (Hugging Face OpenAI etc.) and design hybrid inference strategies combining local and cloud models for optimal performance.

Requirements

Education: Bachelors or Masters degree in Computer Science Artificial Intelligence or related field.
Experience: 2 years of professional experience in AI/ML or relevant domains with a proven track record of developing training and deploying machine learning or deep learning models in real-world environments.
Excellent understanding of classical ML (scikit-learn): regression classification clustering; able to design experiments and baselines.
Strong expertise in Computer Vision: object detection segmentation OCR pipelines (training from scratch and transfer learning).
Deep understanding of model optimization: quantization pruning distillation FLOPs analysis CUDA profiling mixed precision and inference performance trade-offs.
Proven ability to design and train models from scratch (not only using pretrained checkpoints): architecture design loss functions training loops and evaluation.
Hands-on experience with LLMs and diffusion-based models (e.g. Stable Diffusion).
Proficiency with ONNX TensorRT TorchScript and serving frameworks (Triton TorchServe or ONNX Runtime).
Skilled in GPU programming and CUDA optimization (profiling with nvprof/nsight memory management multi-GPU setups).
Strong backend engineering in Python (FastAPI Flask) async programming WebSockets/SSE and RESTful API design.
Experience with containerization and orchestration (Docker Kubernetes Helm) and deploying GPU workloads to AWS/GCP/Azure or on-prem clusters.
Solid software engineering discipline: CI/CD testing code reviews reproducibility and version control.
Nice-to-Have: Familiarity with privacy-preserving ML (differential privacy federated learning) and observability tools like Prometheus Grafana Sentry or OpenTelemetry.
Collaborative open to knowledge-sharing and teamwork.
Team Player willing to support peers and contribute to collective success.
Growth Minded eager to learn improve and adapt to emerging technologies.
Adaptable flexible in dynamic fast-paced environments.
Customer-Centric focused on delivering solutions that create real business value.

DescriptionDevsinc is hiring a skilled AI & ML Engineer with more than 2 years of professional experience in building and fine-tuning Generative AI models (LLMs Diffusion Models) Vision-Language Models (VLMs) and both classical and deep learning systems developing solutions from scratch and taking t...

Description

Develop production inference stacks: Convert and optimize models (Torch ONNX TensorRT) quantize/prune profile FLOPs and latency and deliver low-latency GPU inference with minimal accuracy loss.
Build robust model-serving infrastructure: Implement FastAPI/gRPC inference services token or frame-level streaming model versioning and routing autoscaling rollbacks and A/B testing.
Create Computer Vision solutions from scratch: Design pipelines for object detection theft detection OCR (document parsing structured extraction) and surveillance analytics; fine-tune Hugging Face pretrained models when beneficial.
Fine-tune Stable Diffusion and other generative models for brand- or style-consistent image generation and downstream vision tasks.
Train and fine-tune Vision-Language Models (VLMs) for multimodal tasks (captioning VQA multimodal retrieval) using both from-scratch and transfer-learning approaches.
Design and adapt LLM-based Generative AI systems for conversational agents summarization RAG pipelines and domain-specific fine-tuning.
Implement MLOps / LLMops / AIOps practices: Automate CI/CD for training and deployment manage datasets and experiments maintain model registries and monitor latency drift and performance with alerting and retraining pipelines.
Develop data acquisition & ingestion pipelines: Build compliant scrapers collectors and scalable ingestion systems with proxy rotation and rate-limit handling.
Integrate third-party models and APIs (Hugging Face OpenAI etc.) and design hybrid inference strategies combining local and cloud models for optimal performance.

Requirements

Education: Bachelors or Masters degree in Computer Science Artificial Intelligence or related field.
Experience: 2 years of professional experience in AI/ML or relevant domains with a proven track record of developing training and deploying machine learning or deep learning models in real-world environments.
Excellent understanding of classical ML (scikit-learn): regression classification clustering; able to design experiments and baselines.
Strong expertise in Computer Vision: object detection segmentation OCR pipelines (training from scratch and transfer learning).
Deep understanding of model optimization: quantization pruning distillation FLOPs analysis CUDA profiling mixed precision and inference performance trade-offs.
Proven ability to design and train models from scratch (not only using pretrained checkpoints): architecture design loss functions training loops and evaluation.
Hands-on experience with LLMs and diffusion-based models (e.g. Stable Diffusion).
Proficiency with ONNX TensorRT TorchScript and serving frameworks (Triton TorchServe or ONNX Runtime).
Skilled in GPU programming and CUDA optimization (profiling with nvprof/nsight memory management multi-GPU setups).
Strong backend engineering in Python (FastAPI Flask) async programming WebSockets/SSE and RESTful API design.
Experience with containerization and orchestration (Docker Kubernetes Helm) and deploying GPU workloads to AWS/GCP/Azure or on-prem clusters.
Solid software engineering discipline: CI/CD testing code reviews reproducibility and version control.
Nice-to-Have: Familiarity with privacy-preserving ML (differential privacy federated learning) and observability tools like Prometheus Grafana Sentry or OpenTelemetry.
Collaborative open to knowledge-sharing and teamwork.
Team Player willing to support peers and contribute to collective success.
Growth Minded eager to learn improve and adapt to emerging technologies.
Adaptable flexible in dynamic fast-paced environments.
Customer-Centric focused on delivering solutions that create real business value.

Key Skills

Industrial Maintenance
Machining
Mechanical Knowledge
CNC
Precision Measuring Instruments
Schematics
Maintenance
Hydraulics
Plastics Injection Molding
Programmable Logic Controllers
Manufacturing
Troubleshooting

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Devsinc

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