AI & Financial Engineering Developer

 

expertise in artificial intelligence/machine learning with quantitative finance and financial

engineering.

edge AI and complex financial systems.

 

* Design, develop, and deploy machine learning models and AI-powered applications for

financial use cases

* Build and optimize deep learning, NLP, and generative AI solutions

* Develop data pipelines and feature engineering frameworks for model training and

inference

* Implement MLOps best practices including model versioning, monitoring, and continuous

deployment

* Stay current with state-of-the-art AI research and evaluate applicability to financial

domains

 

* Develop quantitative models for pricing, risk management, and portfolio optimization

* Implement algorithmic trading strategies and backtesting frameworks

* Build financial simulation engines (Monte Carlo, stochastic modeling, etc.)

* Design and develop derivatives pricing models and fixed-income analytics

* Create real-time market data processing and analytics systems

 

* Write production-quality, scalable, and maintainable code

* Architect and build high-performance distributed systems

* Develop RESTful APIs and microservices for financial applications

* Implement robust testing, CI/CD pipelines, and documentation practices

* Collaborate with cross-functional teams including traders, quants, risk managers, and data

engineers

 

* Education: Master’s or PhD in Computer Science, Financial Engineering, Quantitative

Finance, Mathematics, Physics, or a related quantitative field

* Experience: 7+ years of professional software development experience, with at least 3

years in AI/ML and 2+ years in financial services or fintech

* Programming Languages: Expert proficiency in Python; strong skills in C++, Java, or Scala

* AI/ML Expertise: Hands-on experience with TensorFlow, PyTorch, scikit-learn, and large

language models (LLMs)

* Financial Knowledge: Strong understanding of financial instruments (equities, fixed

income, derivatives, structured products), market microstructure, and quantitative risk

measures (VaR, Greeks, CVA)

* Mathematics: Advanced knowledge of stochastic calculus, linear algebra, probability

theory, and numerical methods

* Data & Infrastructure: Experience with SQL/NoSQL databases, cloud platforms (AWS,

Azure, or Google Cloud Platform), and big data technologies (Spark, Kafka)

 

* CFA, FRM, or equivalent financial certification

* Experience with reinforcement learning applied to trading or portfolio management

* Knowledge of blockchain/DeFi protocols and smart contract development

* Familiarity with regulatory frameworks (Basel III/IV, MiFID II, Dodd-Frank)

* Publications in AI/ML or quantitative finance journals

* Experience with real-time streaming systems and low-latency architectures

* Proficiency with LLM fine-tuning, RAG architectures, and AI agents for financial

Applications