Software Engineer - Artificial Intelligence/Machine Learning at Devsinc

Lahore, Punjab, Pakistan -

Full Time

Start Date

Immediate

Expiry Date

14 Feb, 26

Salary

0.0

Posted On

16 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Artificial Intelligence, Machine Learning, Computer Vision, Model Optimization, Generative AI, Deep Learning, MLOps, Python, FastAPI, Docker, Kubernetes, CUDA, LLMs, Diffusion Models, OCR, Data Acquisition

Industry

IT Services and IT Consulting

Description

Devsinc is hiring a skilled AI/ML Engineer with at least 3 years of hands-on 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. You’ll 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. Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or related field. Experience: Minimum 3 years of professional experience in AI/ML or related domains. 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, including 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. Understanding of classical ML techniques (regression, classification, clustering) and experiment design. 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.

Responsibilities

Develop production inference stacks and build robust model-serving infrastructure. Create Computer Vision solutions and implement MLOps practices for model training, deployment, and monitoring.