Jobs

A Middle Systems / Business Analyst with 3-5 years of experience across automotive, bank financial projects and medical device log parsers.
Strong knowledge of system analysis (SDLC), UML, flowcharting, and requirements engineering from scratch.
Proficient in documentation (SRS), functional specs, impact analysis, and strict release quality ompliance.
Familiar with ETL pipelines, machine data log file processing, and data structures within the AWS Cloud ecosystem.
Expert in Jira, Confluence, and utilizing AI-driven tools to automate repetitive workflows.
Strong problem-solving skills with a history of adapting to shifting client priorities.
Excellent communication skills, driving cross-functional alignment between business, engineering, DevOps, and QA teams.
 

Job Responsibilities

Analyze, define, and document system requirements, specifications (SRS), and workflows from scratch
by collaborating directly with device product teams.
Work with cross-functional teams (BE, FE, DevOps, and testing) to ensure system features align with business objectives and customer needs.
Identify and resolve system performance bottlenecks, technical debt, and compatibility issues.
Support the development team by managing the backlog,clarifying requirements, and ensuring system feasibility.
Ensure compliance with strict internal quality procedures, industry standards, and best practices for release documentation packages.
Collaborate with QA teams to design test cases and verification files that validate system behavior.
Assist in data validation, log analysis, and parser verification to improve overall system functionality and data reliability.
 

Department/Project Description

The global leader in health care, dedicated to helping people. The largest healthcare device company in the world, with complementary businesses in Surgical Care, research and developer to meet customer needs and patient demands.
You will have an opportunity to become a part of the fast-growing HealthTech Division and create innovative solutions for MedTech and HealthTech domains.

The Parsers Project is focused on delivering a robust, scalable, and automated framework for generating test cases to validate log file parsers. This initiative enhances accuracy, reliability, and efficiency in parser validation by incorporating structured data transformation, automated test execution, and comprehensive verification mechanisms. The solution offers an end-to-end workflow that extracts, transforms, and verifies log data while ensuring compliance with anonymization and normalization standards. By integrating advanced data processing techniques and automated validation methods, the project streamlines parser testing, reduces manual intervention, and improves overall quality assurance processes.

We are looking for a Computer Vision / Generative AI Engineer with strong hands-on experience in generative image models and production-grade computer vision pipelines.

The candidate should be able to work deeply with modern image generation models, understand how they work under the hood, adapt them to specific product needs, and turn research or experimental ideas into reliable working systems.

Key responsibilities

• GenAI R&D: Adapt, fine-tune, and deploy FLUX and diffusion models.
• CV Pipelines: Build controlled generation workflows combining GenAI with detection and segmentation.
• Paper-to-Code: Implement cutting-edge research into production-ready features.
• Inference Optimization: Speed up models and reduce GPU costs (quantization, compilation, caching).
• Data Prep: Collect, filter, and annotate high-quality datasets for training.
• API & Deployment: Containerize ML services (Docker) and build scalable APIs.

Key skills and experience

• Strong understanding of modern transformer-based image generation architectures, especially FLUX-based models, including their architecture, training logic, inference process, and practical limitations.
• Ability to build custom inference pipelines on top of base generative models and adapt them to specific product or business requirements.
• Experience building end-to-end computer vision pipelines: reading research papers, documenting findings, collecting and preparing datasets, training models, validating results, and iterating on model quality.
• Understanding of inference optimization concepts such as quantization, compilation, and caching.
• Practical experience with Python, PyTorch, Diffusers, and OpenCV.
• Experience packaging ML/CV solutions into Docker containers and exposing them through APIs for testing and production use.
• Experience working with modern image generation APIs is a strong plus, including Nano Banana and GPT Image.
• Solid general computer vision background and familiarity with common CV tasks such as detection, segmentation and classification.