Role Description As a Junior Computer Vision Engineer at InnovationTeam, you will be responsible for designing, developing, and deploying vision-based AI systems for real-world, production environments. You will work closely with cross-functional teams to build scalable image and video analytics solutions. - Develop and train Computer Vision models for image and video analysis. - Implement solutions for object detection, image classification, segmentation, and tracking. - Prepare and manage datasets, including data cleaning, labelling, and augmentation. - Optimize models for GPU performance, inference speed, and scalability. - Deploy AI models into production using APIs and containerized services. - Integrate Computer Vision models with broader AI and analytics platforms. - Collaborate with software engineers, data scientists, and product teams. - Maintain documentation and ensure code quality and reproducibility. Qualifications - Minimum 5 years of hands-on experience in AI / Machine Learning/Deep learning with a focus on Computer Vision. - Master’s degree in computer science, software Engineering, Artificial Intelligence, Engineering, or a related field. - Strong knowledge of Computer Vision concepts and algorithms. - Practical experience with CNN-based models and modern CV architectures. - Proficiency in Python and common ML/CV libraries (PyTorch or TensorFlow, OpenCV). - Experience with object detection and segmentation frameworks (e.g., YOLO, Detectron2). - Understanding of model evaluation, metrics, and performance optimization. - Experience deploying models in cloud or on-prem environments. - Familiarity with Docker and basic MLOps practices. - Excellent English communication skills (written and spoken). Requirements - Experience with video analytics or real-time inference. - Exposure to Vision Transformers or multimodal AI. - Experience with GPU-based training and inference. - Knowledge of cloud platforms (OCI, AWS, Azure, or GCP). - Background in domains such as healthcare, smart cities, or industrial AI. - Work on practical, production-grade Computer Vision solutions. - Access to GPU infrastructure and modern AI tooling. - Collaborative, engineering-focused work environment. - Opportunities for growth and advanced AI exposure. - Competitive compensation package.

• Design and build AI agent pipelines and maintain RAG systems • Integrate and optimize Large Language Models and structured output workflows • Design, develop, and deploy product features and scalable backends • Collaborate with clients and stakeholders to translate business requirements into software solutions • Own observability and quality metrics for AI systems

• Build and maintain the services, data models, and APIs that power the platform — designed for correctness, testability, and scale. • Work on the systems that coordinate complex, multi-step interactions between AI agents and external systems, improving their reliability and throughput. • Design systems that grade agent outputs, combining deterministic checks with model-assisted judgment — and make scoring reliable, explainable, and reproducible. • Build pipelines that generate, transform, and quality-check large volumes of structured data and benchmark content. • Add the tests, instrumentation, and safeguards needed to trust outputs from systems that are inherently non-deterministic.

Role Description As a fast-growing scale-up, our team of 100+ tech specialists—including Data Engineers, Data Scientists, and more—delivers innovative analytics solutions across industries like FMCG, retail, manufacturing and FSI. Together with a partnering company, we're looking for a strong Software Engineer to build the systems behind an AI agent evaluation platform — systems that test, grade, and stress AI agents at scale, and turn the results into actionable signal about how those agents behave. This is a software engineering role first — designing services, data models, and pipelines that have to be correct, tested, and maintainable — applied to an AI problem domain. You'll work with LLMs regularly, but the core of the job is engineering, not prompt-tuning. What Your Day Might Look Like: - Backend services & APIs: Build and maintain the services, data models, and APIs that power the platform — designed for correctness, testability, and scale. - Simulation & orchestration: Work on the systems that coordinate complex, multi-step interactions between AI agents and external systems, improving their reliability and throughput. - Evaluation & scoring: Design systems that grade agent outputs, combining deterministic checks with model-assisted judgment — and make scoring reliable, explainable, and reproducible. - Data pipelines: Build pipelines that generate, transform, and quality-check large volumes of structured data and benchmark content. - Quality & reliability: Add the tests, instrumentation, and safeguards needed to trust outputs from systems that are inherently non-deterministic. Qualifications - 4+ years building and shipping production software, with strong proficiency in Python. - Deep software engineering fundamentals: system and API design, data modeling, concurrency/async, testing strategy, debugging, and code review. - Experience designing and operating distributed or service-oriented systems (queues, workers, APIs) — not just calling them. - Comfort designing schemas and working with relational databases, plus the migrations and performance concerns that come with them. - Working knowledge of LLM APIs — orchestration, structured outputs, and handling non-determinism. - Ability to reason about correctness of probabilistic systems: how to test, measure, and trust outputs that aren't byte-for-byte deterministic. - High quality bar: you write tests, types, and docs by default, and you keep changes small and reviewable. Requirements - Bonus points for experience building agentic or multi-agent systems, tool-use, or orchestration frameworks. - Background in evaluation / benchmarking of ML or LLM systems (rubrics, golden datasets, model-as-judge, inter-rater reliability). - Experience with distributed task queues and async workloads. - Modern Python tooling and typed codebases (e.g. type checkers, linters, Pydantic, FastAPI). - Retrieval / search experience and working with data ingest pipelines. - Some comfort with the infra side (Docker, CI/CD) so you can ship what you build. Benefits - Competitive salary. - Training budget to level up your skills from top tech partners like Microsoft, AWS, Salesforce, and Databricks – whether it’s certifications or courses, we’ve got you covered. - Private insurance, top-tier tech gear, and the chance to work with a stellar crew.