About Algotale
At Algotale, we believe in the transformative potential of data to reshape industries, drive innovation, and create unparalleled value. Established in 2020 by a team of passionate and visionary professionals, Algotale set out to redefine the landscape of IT services and consulting by integrating a data-centric approach into every solution we design.
Our journey began with a focus on creating customized, data-driven solutions that directly address the unique challenges and ambitions of our clients. Over the years, our dedication to excellence has allowed us to grow into a trusted partner for businesses across industries, supporting them through every stage of digital transformation. Today, Algotale stands as a prominent player in the IT consulting world, known for our agility, innovation, and commitment to delivering results.
With a team of over 500 skilled professionals, we specialize in IT services, staffing, and consulting, empowering our clients to unlock the full potential of their data. Our expertise extends across data analytics, artificial intelligence, machine learning, and cutting-edge cloud solutions. By leveraging these technologies, we help organizations build resilient digital infrastructures, optimize operations, and achieve strategic growth.
As the digital landscape evolves, so does our commitment to pushing boundaries, embracing new challenges, and exceeding client expectations. At Algotale, we don’t just provide solutions; we forge partnerships built on trust, insight, and a shared drive to thrive in an increasingly data-driven world.
Website: www.algotale.com
Industry: IT Services and Consulting
Company Size: 501–1,000 employees
Founded: 2020
Specialties: IT Services, IT Staffing, IT Consulting
Role:- Python ML Engineer
Exp range:- 8 years
Location:- Remote WFH (Office location is Oman)
Skills required:- Python,Pytorch,ML
Working Days:- Sunday to Thursday. (Fri & Sat remains off)
Job Title
Senior ML Engineer
Role Summary
We are looking for a hands-on Computer Vision and ML Engineer with deep expertise in deep object detection and strong production delivery skills. You will own end-to-end detection systems, from dataset and training pipelines to optimized inference services, monitoring, and continuous improvement.
Key Responsibilities
- Design, train, debug, and improve state-of-the-art object detection models for real-world conditions
- Build robust training pipelines: datasets, augmentation, caching, versioning, and reproducible experiments
- Perform systematic error analysis and ablations to isolate failure modes (data vs model vs inference vs post-processing)
- Develop custom detection systems beyond standard training, including multi-stage pipelines, ensembles, and specialized post-processing
- Optimize inference for latency, throughput, and memory, including GPU acceleration and export toolchains
- Deliver production-grade services using Docker, Linux, CI/CD, and APIs (FastAPI and/or gRPC)
- Implement testing strategy across the pipeline (unit, integration, regression), including “golden image” test sets
- Set up monitoring and maintenance: logging, metrics dashboards, drift/performance tracking, retraining triggers
- Write clear technical documentation, architecture decisions, and trade-off analyses
- Read research papers and rapidly translate ideas into working prototypes and deployable components
Required Skills and Experience
Python and ML Engineering
- Advanced Python engineering: clean architecture, packaging, typing, testing, profiling
- Strong PyTorch experience (must)
- TensorFlow optional
- Strong model debugging skills and disciplined experimentation
- Experiment tracking and reproducibility: W&B and/or MLflow, deterministic runs, seed control
- Config management: Hydra and/or OmegaConf
- Data pipelines: PyTorch Dataset/DataLoader, augmentation pipelines, caching
- Dataset versioning: DVC or equivalent
Computer Vision Fundamentals
- Strong CV fundamentals: preprocessing, geometry, photometric effects, distortions, camera models
- OpenCV expertise for classical CV and integration into modern ML pipelines
- Evaluation expertise: mAP, precision/recall, IoU, PR curves, calibration
Deep Object Detection Expertise
- Hands-on experience with modern detectors such as: YOLO (v5/v8/v9), Faster R-CNN, RetinaNet, EfficientDet, DETR variants
- Experience building advanced detection workflows:
- Multi-stage detection (proposal, refine, classify)
- Ensemble and stacking strategies
- Specialized post-processing tuned to domain constraints
Production ML and MLOps Delivery
- Model export and serving: ONNX export/runtime, plus at least one of TorchScript or TensorRT
- GPU inference optimization and performance tuning (batching, throughput, latency, memory)
- Deployment: Docker, Linux, CI/CD basics (GitHub Actions and/or GitLab CI)
- Service implementation: FastAPI and/or gRPC, model versioning, rollback strategy
- Monitoring and lifecycle: drift/performance monitoring, logging, dashboards, retraining triggers
- Testing: unit tests for preprocessing/post-processing, integration tests, regression sets, threshold stability tests
R&D Capability
- Ability to read papers and implement ideas quickly
- Strong debugging methodology, ablation design, and error analysis
- Clear technical writing and engineering decision-making
Nice-to-Have (Strong Bonuses)
Engineering Drawings Domain
- Experience with engineering drawings and technical documents
- PDF vector vs raster workflows, line detection, symbol detection
- Table/diagram understanding, CAD-like concepts, annotation workflows
- OCR + vision hybrid systems (even if not OCR-first)
Document and Diagram Vision Toolchain
- PyMuPDF and/or pdfplumber
- Image rasterization, coordinate transforms
- Handling noisy scans: skew/warp correction, deskewing
Broader CV Capabilities
- Instance segmentation: Mask R-CNN, YOLO-seg
- Keypoints, pose, landmark detection
- Tracking for video: ByteTrack, DeepSORT
