Python Engineer

Python Engineer

Oddin.gg

About Valka   Valka, a visionary spin-off from the Realms Group (the parent company of Oddin.gg), is on a mission to revolutionize the way people create and experience digital content. Our team believes that content shouldn’t just be consumed; it should be co-created in real time, blurring the lines between imagination and reality. By harnessing the power of cutting-edge AI, we aim to build an interactive human-digital platform where virtual characters respond dynamically to each user’s voice, text, gestures, and more.   This is your chance to join a diverse group of innovators who are driven to redefine what’s possible in generative content. Together, we’re changing the paradigm from passive viewing to active participation, unlocking new creative frontiers across gaming, entertainment, education, and beyond.

Python Engineer – Market Microstructure Tooling

Qube Research & Technologies

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a group driven by technology and data, implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. You will work on systems and tooling at the intersection of trading infrastructure and market microstructure. The role focuses on building data pipelines, analytics and libraries that support execution analysis and improve trading performance. You will operate close to the trading stack, working with high resolution datasets and contributing to both research and production systems. Your future role within QRT * Analyse trading behaviour across venues to identify execution inefficiencies and improve latency and fill performance * Develop Python libraries, data pipelines and tooling to process exchange protocols, order flow and large scale trading data * Build analytical frameworks, metrics and monitoring tools to evaluate execution quality and system performance * Contribute to production systems supporting live trading, including data validation, pipeline reliability and real time monitoring * Collaborate with stakeholders to translate analytical insights into measurable improvements in execution performance * Work with research and infrastructure teams to support ongoing development of execution analysis and market behaviour understanding Your present skillset * Degree in Computer Science, Engineering, Mathematics or a related quantitative field * 7+ years of professional experience in Python development, building libraries, data platforms or analytical tooling * Strong experience working with large scale and time series datasets using tools such as NumPy and Pandas * Solid SQL knowledge and experience working with analytical databases * Strong problem solving skills with the ability to investigate complex data and system behaviours * Ability to translate analytical or trading related problems into robust engineering solutions * Experience working in Linux environments and applying software engineering best practices such as testing, version control and continuous integration * Strong communication skills with the ability to collaborate across technical and non technical stakeholders * Familiarity with financial markets, electronic trading or execution analysis * Exposure to network protocols, packet analysis or performance optimisation techniques is beneficial * Experience with lower level programming languages such as C, C++ or Rust is advantageous QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work life balance.

AI/ML Engineer

Kallikor

At Kallikor, we're building the future of supply chain intelligence through AI-powered simulation digital twins. We create living digital representations of real-world operations (warehouses, distribution networks, global logistics) that help organisations make better decisions faster. We're at an inflection point: moving from AI-assisted tools to domain-specific AI that understands supply chains as deeply as our best engineers do. You'll be instrumental in building our first domain-specific language model (DSLM) and the foundation for Project Genome, an ambitious initiative to capture and synthesise the world's supply chain knowledge into actionable intelligence. This is a production engineering role first. You'll build robust Python systems that happen to train and serve LLMs, not the other way around. We need someone who writes production-quality code, debugs complex distributed systems, and thinks about reliability, who has learned ML/LLMs as powerful tools in their engineering arsenal. You'll work across our entire AI stack: building FastAPI services that serve models, creating training pipelines that process production data, deploying inference endpoints with proper monitoring, and integrating all of this into our existing Python backend. The ML is important, but the engineering discipline is what makes it production-ready. Learn more at kallikor.ai. YOUR OPPORTUNITY * Build production AI systems: Design and implement the full stack, from FastAPI endpoints that handle requests, to training pipelines that process data, to inference services that serve predictions. You'll own the architecture, not just the model weights. * Train and deploy our DSLM: Fine-tune models using Unsloth/Axolotl, but more importantly, build the robust infrastructure around it - data pipelines that feed training, evaluation frameworks that catch regressions, deployment systems that handle failover. Make it production-grade. * Integrate ML into our backend: We use FastAPI, PydanticAI, FastMCP, Memgraph. You'll extend these systems with ML capabilities, not as a separate "ML service" but as a natural part of our backend architecture. Clean abstractions, proper error handling, observability. * Own inference performance: Get models running fast, whether that's vLLM deployment, quantization strategies, batching optimizations, or caching. Hit our <200ms latency targets through engineering, not just throwing bigger GPUs at it. * Shape Project Genome's foundation: Work with our Principal Engineer to architect how we ingest, process, and learn from global supply chain data. This is systems design as much as ML with data pipelines, graph databases, incremental learning strategies being just as important. * Mentor through code review and pairing: Raise the bar on code quality, testing, and production practices across the team. Teach mid and junior engineers how to build ML systems that don't fall over. WHY YOU'RE MADE FOR THIS * You're a strong production Python engineer: You write clean, maintainable, tested code. You understand async/await, know when to use generators vs lists, can profile performance bottlenecks. You've built FastAPI services (or similar) that handle production traffic. Your code passes review without drama. * You've built with LLMs in production: You've integrated GPT-4/Claude into real applications, handled streaming responses, dealt with rate limits and retries, cached intelligently. You know the practical challenges: prompt engineering, context management, error handling, cost control. * You've trained or fine-tuned models: Whether it's fine-tuning LLMs, training classifiers, or running experiments, you understand the workflow. You've dealt with training data quality, evaluation metrics, and overfitting. You can debug why a model isn't learning what you expected. * You think like a systems engineer: You design for failure, add instrumentation, consider edge cases. You know that "the model works on my laptop" isn't shipping. You care about monitoring, logging, alerting, and graceful degradation. * You can navigate the ML landscape pragmatically: You know enough about transformers, attention mechanisms, and training dynamics to make informed decisions. But you're not precious about it. If a simple heuristic beats a complex model, you ship the heuristic. * You balance velocity with quality: You ship incrementally and iterate based on production data. But you don't accumulate tech debt, you refactor proactively, write tests that matter, and leave the codebase better than you found it. * You communicate trade-offs clearly: You can explain to the team why we're choosing LoRA over full fine-tuning, why we're deploying on Fireworks instead of self-hosting, or why a 7B model might beat a 70B model. You help everyone make informed decisions. WHAT WE'RE LOOKING FOR SPECIFICALLY Must have: * 5+ years building production Python systems (backend services, APIs, data processing) * Strong software engineering fundamentals: design patterns, testing, debugging, profiling * Experience integrating LLMs into applications (OpenAI/Anthropic APIs, prompt engineering, streaming, PydanticAI) * Understanding of ML training workflows (even if you're not an expert. You need to know enough to build the infrastructure) * Docker, CI/CD, production deployment experience * Can read and understand PyTorch code (you don't need to write novel architectures) Nice to have: * Fine-tuning experience (LoRA, full fine-tuning, QLoRA) * Distributed training basics (DeepSpeed, FSDP) * Graph databases (Memgraph, Neo4j) * Supply chain or logistics domain knowledge * Experience with agent frameworks (LangChain, PydanticAI, etc.) WHAT YOU'LL WORK WITH * Backend Stack: Python, FastAPI, PydanticAI, FastMCP, Memgraph, Postgres * ML Stack: PyTorch, Unsloth/Axolotl for training, vLLM for inference, Weights & Biases * Models: Qwen 2.5, Llama 3.1, GPT-4, Claude (for now) * Infrastructure: AWS (flexible), Docker, Kubernetes, GPUs when needed * Team: Principal Engineer (your partner on architecture), Mid Data/ML Engineer (your data pipeline partner), Junior AI Engineer (your mentee) EXAMPLE PROJECTS YOU'LL OWN * Build a FastAPI service that handles streaming LLM responses with correct error handling and retry logic * Create a training pipeline that processes production logs, validates data quality, and triggers fine-tuning runs * Deploy a fine-tuned 7B model with vLLM that beats GPT-4 latency while maintaining quality on our domain * Design the data ingestion architecture for Project Genome, how we process papers, documentation, and operational data at scale * Implement evaluation frameworks that catch model regressions before they reach production About Us Kallikor is determined to foster an environment where people can do their best work and feel like they belong. We believe a healthy culture, strong values and contribution from a diverse range of individuals will help us to achieve success. We do not discriminate based on race, ethnicity, gender, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.