SE AI/ML at Devsinc

Lahore, Punjab, Pakistan -

Full Time

Start Date

Immediate

Expiry Date

18 Jan, 26

Salary

0.0

Posted On

20 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Learning Engineering, Generative AI, Computer Vision, Model Optimization, MLOps, LLMops, Deep Learning, FastAPI, Docker, Kubernetes, Python, CUDA, Data Acquisition, Model Serving, Third-party Integration, CI/CD

Industry

IT Services and IT Consulting

Description

We’re hiring a hands-on AI Learning Engineer who can build and fine-tune generative AI (diffusion & LLMs ), vision-language models (VLMs), classical & deep models from scratch, and productionize them end-to-end. This role blends modeling (you’ll train and fine-tune models) with production systems (MLOps, LLMops, model optimization, serving, and API/backends). You will not only use pre-trained models, you will design, train, optimize, and serve custom models for production use (GenAI, Stable Diffusion, OCR, theft detection, recommenders, etc.). Develop production inference stacks: convert & optimize models (Torch → ONNX → TensorRT when appropriate), quantize/prune, profile FLOPs and latency, and deliver low-latency GPU inference with minimal accuracy loss. - Create robust model serving infrastructure: FastAPI / gRPC services for inference, streaming outputs (token-level streaming for LLMs, frame/segment streaming for CV), model versioning and routing, autoscaling, model rollback and A/B testing. - Build CV solutions from scratch: object detection, theft/theft-detection pipelines, OCR (document parsing, structured extraction), surveillance analytics, and integrate + finetune Hugging Face pretrained models when beneficial. - Fine-tune Stable Diffusion and other generative image models for brand/style-consistent image generation and downstream tasks. - Train and fine-tune VLMs (vision-language models) for multimodal tasks (captioning, visual QA, multimodal retrieval), using both from-scratch training and transfer learning from HF checkpoints. - Design, train & fine-tune GenAI models (LLMs) for use cases such as conversational agents, summarization, retrieval-augmented generation (RAG), and domain adaptation. - MLOps / LLMops / AIOps: CI/CD for training & deployment, dataset versioning, experiments tracking, model registry, monitoring (latency, throughput, model drift, data drift), alerting and automated retraining pipelines. - Data acquisition & pipeline work: build scrapers/collectors and scalable ingestion pipelines; implement proxy pools, rate limit handling, and rotation for reliability (with compliance & respect for target site terms). - Third-party model integration: call and compose third-party inference APIs (Hugging Face, OpenAI, other vendors), build fallback & hybrid inference strategies that combine local and cloud models. Required qualifications: - Strong experience with computer vision: object detection, segmentation, OCR pipelines (training from scratch and transfer learning). - Deep knowledge of model optimization: quantization, pruning, distillation, FLOPs analysis, CUDA profiling, mixed precision (AMP), and inference time tradeoffs. - Demonstrated ability to design & implement models from scratch (not only using pretrained checkpoints): architecture design, loss selection, training loops, evaluation metrics. - Practical experience training and fine-tuning LLMs (transformers) and generative image models (Stable Diffusion or diffusion frameworks). - Experience exporting & running models with ONNX, TensorRT, TorchScript, and familiarity with Triton, TorchServe, or ONNX Runtime for production serving. - Hands-on with GPU infrastructure and CUDA (profiling with nvprof/nsight, memory management, multi-GPU training). - Solid backend engineering skills: Python, FastAPI (or Flask), asynchronous programming, WebSockets/SSE, REST design. - Containerization and orchestration: Docker, Kubernetes, Helm, and experience deploying GPU workloads to AWS / GCP / Azure or on-prem. - Good understanding of classical ML (scikit-learn): regression, classification, clustering; able to design experiments and baselines. - Strong software engineering practices: unit tests, CI/CD, code reviews, reproducibility. - Excellent communication skills, able to explain ML tradeoffs to product and frontend teams. Preferred / Nice-to-have: - Knowledge of privacy-preserving ML (DP, federated learning) or regulatory constraints for data handling. - Experience with logging & observability: Prometheus, Grafana, Sentry, OpenTelemetry.

Responsibilities

The role involves building and fine-tuning generative AI and vision-language models, as well as developing production inference stacks and robust model serving infrastructure. You will also be responsible for training and optimizing models for various applications, including computer vision and generative tasks.