POSITION SUMMARY
This position is seeking an experienced Software Developer to create intelligent algorithms for trading, using the data from internally created model, translates the technical prototype model into algorithms to capture simulated market condition into automation, developing high-performance, low-latency, AI auto trading algorithms to strengthen existing algorithms and machine learning. The role involves designing, building, and integrating market data feeds, and cross functional collaboration with traders, analysts and speedy execution to deliver scalable, robust automated trading solutions. Leading design, development, and deployment of machine learning systems, bringing technical knowledge and experience into the team, and using this to create real solutions to real trading problems. Hands-on experience with agentic AI development and orchestration, Strong understanding of LLM prompt engineering, embeddings, coding, and foundational Retrieval-Augmented Generation (RAG) concepts. Proficient quant in Python (NumPy, Pandas, SciPy), PyTorch/TensorFlow/JAX, and high-performance computing (multi-GPU, distributed training). Large-scale data engineering, signal processing, and mathematical foundations including optimization, statistics, and numerical methods. Extensive experience with Large Language Models, Agentic AI, and Reinforcement Learning.
Key Responsibilities
- Develop, optimize, and maintain algorithmic trading systems.
- Translate trader requirements into software products and prototypes.
- Productionize quantitative models with monitoring, back testing, and validation.
- Contribute to system architecture, code reviews, and technical mentorship of junior team members.
- Ensure adherence to coding standards, agile practices, and robust software engineering principles.
- Design, develop, and maintain pre-trade applications supporting commodities trading desks.
- Build pricing tools, trading algorithms, and quantitative libraries in C#
- Collaborate closely with traders, quants, and analysts to gather requirements and deliver solutions aligned with trading strategies.
- Enhance and optimize existing trading systems to improve performance, scalability, and reliability.
- Develop real-time and low-latency components for pricing and trading decision support
- Participate in architecture design, code reviews, and best practice implementation
- Support deployment, testing, and production releases.
- Develop and deploy prompt-based single-agent systems for targeted workflows.
- Integrate AI models into subsystems supporting transactions, knowledge management, and service operations.
- Design and implement multi-agent orchestration for advanced RAG pipelines.
- Build and maintain API microservices to expose AI capabilities (e.g., entity extraction).
- Collaborate on dataset creation for model fine-tuning and continuous improvement.
- Partner with domain experts to enhance model accuracy, mitigate hallucinations, and ensure compliance.
Required Skills & Experience
- 7+ years in software development, with 4+ years productionizing trading models.
- Proficient in C#/.NET, multithreading, low-latency and asynchronous applications.
- Knowledge of market microstructure, front-office algo trading, orderbooks, and exchange APIs.
- Experience with backtesting, walk-forward optimization, and modular trading system design.
- STEM degree (Computer Science, Engineering, Mathematics, or related).
- 5+ years of professional experience in C# / .NET development.
- Strong experience developing front-office or pre-trade trading applications
- Experience working in Commodities Trading, Energy Trading, or Financial Markets
- Strong understanding of object-oriented programming, multi-threading, and performance optimization.
- Experience working directly with trading desks and business users
- Strong skills in SQL and data handling.
- Experience in Quantitative Development or Algorithmic Trading.
- Knowledge of financial instruments and derivatives, ideally in commodities (Power, Gas, Oil, etc.).
- Experience building pricing models, analytics libraries, or trading algorithms.
- Experience with Python for quantitative or analytics development.
- Familiarity with real-time systems, messaging systems, or event-driven architectures.
- Knowledge of cloud environments (Azure or AWS) is a plus.
- Cloud platform experience, ETRM systems, or energy trading knowledge (power, gas).
- Experience with auto TRADER, Trayport, or other trading platforms.
- UI development (Blazor) and data strategy/governance knowledge.
- Build LLM-powered features (prompt design, RAG pipelines, tools/plug-ins, evaluations, guardrails).
- Experiment with agentic AI patterns (tool use, planning/re-planning, multi-agent workflows) and ship reliable agents.
- Implement and evaluate machine-learning models (classification, regression, clustering, NLP, CV) from prototype to production.
- Write clean, well-tested Python code for data processing, modeling, and service APIs.
- Package and deploy models/services on AWS (e.g., S3, Lambda, ECS/EKS, SageMaker) with basic CI/CD.
- Design simple, efficient data pipelines and integrate with databases (SQL/NoSQL) and vector stores.
- Monitor models in production (latency, drift, quality) and iterate based on telemetry and user feedback.
- Read papers/blogs/specs and quickly translate ideas into working prototypes.
- Strong foundation in algorithms and data structures; able to analyze time/space complexity and choose the right approach.
- Solid understanding of core ML principles: bias/variance, feature engineering, cross-validation, regularization, evaluation metrics.
- Familiarity with LLMs: tokenization basics, model families, fine-tuning concepts, RAG patterns, and LLM evaluations.
- Exposure to agentic AI concepts: tool calling, planning, memory, and simple multi-agent orchestration.
- Knowledge of Model Context Protocol (MCP) for context sharing, secure integrations, and tool orchestration.
- Proficiency in Python and common libraries (NumPy, pandas, scikit-learn; plus, PyTorch or TensorFlow preferred).
- Comfort with AWS fundamentals (IAM, S3, compute/container runtimes) or equivalent cloud experience.
- Experience with databases: writing efficient SQL, understanding normalization/indices; basic NoSQL (e.g., DynamoDB) awareness.
- Familiarity with vector databases (e.g., FAISS, Pinecone, Milvus) is a plus.
- Version control (Git) and basic software craftsmanship (testing, linting, code reviews).
- Data engineering basics: Airflow/Prefect, message queues, data validation.
- API development (FastAPI/Flask) and simple observability (logs/metrics/traces).
- Security, privacy, and responsible-AI awareness (PII handling, prompt injection basics, red-teaming mindset).
- Math comfort: linear algebra, probability, calculus.
Key Competencies
- Strong analytical and problem-solving skills
- Ability to work in a front-office, fast-paced trading environment
- Strong communication skills and ability to interact with traders and quants
- Autonomous, proactive, and delivery-focused mindset
- Strong ability to adapt to new technologies and rapidly learn by reading docs/papers and implementing new ideas.
- Bias for action: iterate quickly, measure results, and improve based on evidence.
- Clear communication and collaborative mindset; comfortable receiving and giving feedback.
Qualifications
- Master's degree in Computer Science, Data Science, Machine Learning, AI/ML, a related field. Ph.D. is a plus
- Fluency in Python with the ability to write clean, modular, well-documented code as well as a solid understanding of coding best practices
- 5-7+ years in industry developing and deploying machine learning models, preferably in NLP, LLM and ideally generative AI domains
- Experience in successfully applying LLMs to solve business problems, solid understanding of Gen AI design paradigms and best practices
- Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or Transformers
- Familiarity with cloud platforms (AWS) and containerization technologies (Docker)
- Excellent problem-solving skills, ability to work independently and in a team
- Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
- Understanding of machine learning fundamentals, including deep learning, generative models, NLP techniques, such as sentiment analysis, and entity recognition and disambiguation
Compensation:
- Highly competitive salary
- 401K
- Medical insurance
