As a Machine Learning Engineer, you will play a pivotal role in designing, developing, and deploying advanced machine learning and generative AI systems that drive innovation across financial technology platforms. You'll work closely with data scientists, product engineers, and domain experts to translate cutting-edge research into scalable, production-grade solutions. Your work will focus on building intelligent systems powered by large language models (LLMs), retrieval-augmented generation (RAG), and agentic architectures, deployed on robust cloud infrastructure. You will contribute to the full lifecycle of ML development—from prototyping and training to deployment, monitoring, and optimization—ensuring models are accurate, reliable, and aligned with business objectives.
Responsibilities
- Design and implement scalable machine learning systems using Python, PyTorch, and modern ML frameworks
- Develop production-ready code that meets performance and reliability standards
- Collaborate with product squads to integrate machine learning capabilities into core platforms
- Share insights and best practices across cross-functional teams
- Build and maintain robust APIs for model serving using tools like FastAPI
- Construct data pipelines using orchestration tools such as Airflow
- Utilize cloud platforms like AWS and Azure to support ML infrastructure
- Develop software components that ingest and process diverse data formats for dynamic ML applications
- Support service level agreements for AI-powered systems
- Use metrics to assess and improve the performance of ML models
- Monitor model behavior in production environments
- Contribute to the stability and effectiveness of deployed systems
- Experiment with generative AI technologies and evaluate their practical applications
- Work with large language models and implement retrieval-augmented generation (RAG) systems
- Explore and prototype agentic workflows driven by language models
- Identify high-value emerging technologies while filtering out overhyped solutions
- Explain ML concepts clearly to technical and non-technical stakeholders
- Partner with data scientists to develop novel AI solutions
- Work alongside senior engineers to design scalable system architectures
- Ensure AI components integrate smoothly into broader business workflows
- Follow software and ML engineering fundamentals including testing, version control, optimization, and containerization
- Adopt and apply architectural best practices in system design
- Seek and incorporate feedback to improve development processes
- Engage in mentorship and learning opportunities with experienced team members
Requirements
- Minimum of three years of professional experience building and deploying production ML systems
- Proven track record of delivering ML solutions from development through to production
- Strong experience writing production-quality Python code with emphasis on code quality, testing, and optimization
- Hands-on experience developing generative AI systems
- Experience working with large language models (LLMs)
- Experience fine-tuning or leveraging LLMs for real-world applications
- Working knowledge of core ML algorithms such as classification, decision trees, SVMs, and neural networks
- Preference for candidates with deep learning expertise
- Familiarity with cloud platforms including AWS and Azure, and services like AWS Bedrock, S3, SageMaker, Azure AI Search, and blob storage
- Ability to use cloud infrastructure for ML and LLM workflows
- Demonstrated success integrating ML solutions into existing products and systems
- Collaborative experience ensuring smooth deployment of AI features
- Experience with containerization tools like Docker, Kubernetes, and AWS EKS for scalable ML deployment
- Bachelor's degree in Machine Learning, Computer Science, Data Science, Applied Mathematics, or a related technical field
Nice to Have
- Master's or higher degree in a relevant technical field is strongly preferred
- Hands-on experience des
Tech Stack
Python, PyTorch, TensorFlow, Hugging Face Transformers, FastAPI, Airflow, Docker, Kubernetes, AWS, Azure, AWS SageMaker, Azure AI Search, S3, Blob Storage, LangChain, RAG (Retrieval-Augmented Generation), LLM (Large Language Models), Agentic Workflows
Benefits
- Competitive base salary and performance bonuses
- Comprehensive health, dental, and vision insurance
- Flexible work hours and remote-friendly policy
- Generous paid time off (PTO) and vacation policy
- Parental leave for new parents
- 401(k) retirement plan with company matching
- Stock options or equity participation
- Annual learning and development stipend
- Onsite and virtual wellness programs
- Free healthy snacks and meals in office
- Employee resource groups and inclusion initiatives
- Annual company retreats and team-building events
- Mental health support and counseling services
- Tuition reimbursement for advanced degrees
- Childcare assistance programs
- Commuter benefits and transportation subsidies
- Discounted gym memberships
- Pet insurance options
- Life and disability insurance coverage
- Flexible spending accounts (FSA) and HSAs
- Volunteer time off (VTO) for community service
- Employee discount programs for tech and travel
- Onsite fitness centers or wellness rooms
- Free financial planning and advisory services
- Relocation assistance for new hires
Work Arrangement
Hybrid (combination of remote and in-office work based on team and project needs)
Team
You will join a high-impact innovation team focused on advancing AI capabilities in financial technology. The team operates in agile squads with cross-functional roles, emphasizing collaboration, rapid prototyping, and continuous delivery. You'll work alongside senior engineers, data scientists, and product managers in a culture that values technical excellence, knowledge sharing, and professional growth.
Additional Information
- This role involves working with sensitive financial data, requiring adherence to strict data governance and security protocols.
- Candidates must be authorized to work in the country where the position is based.
- The company supports visa sponsorship for qualified international candidates in select locations.
- We are committed to building a diverse, equitable, and inclusive workplace.
- Interview process includes technical screening, system design exercise, and cultural fit discussion.
- Onboarding includes structured training on internal tools, security policies, and team workflows.
