qode.world

AI Engineer

• Design and build scalable AI platforms supporting LLMs, RAG pipelines, and multi-model orchestration
• Develop reusable frameworks for prompt management, model routing, evaluation, and monitoring
• Implement LLMOps / MLOps pipelines for continuous integration, deployment, and lifecycle management
• Architect API-first AI services for enterprise-wide consumption
• Integrate and optimize models from providers like OpenAI, Anthropic, Google DeepMind, and open-source ecosystems
• Build multi-model strategies (closed + open source) for performance, cost, and governance
• Implement advanced techniques:
• - Retrieval-Augmented Generation (RAG)
• - Tool use / agents
• - Fine-tuning and embeddings
• - Context optimization and memory systems
• Design systems aligned with security, compliance, and data privacy requirements
• Implement guardrails, auditability, and explainability in AI workflows
• Enable safe AI deployment in distributed environments (e.g., advisor desktops, hybrid cloud)
• Build AI-driven use cases such as:
• - Intelligent document processing (e.g., wealth plans, research docs)
• - Advisor copilots and decision support systems
• - Knowledge assistants and enterprise search
• Partner with business teams to translate use cases into scalable AI solutions
• Develop evaluation frameworks for accuracy, hallucination detection, and model performance
• Optimize latency, throughput, and cost for production deployments
• Establish benchmarking and observability standards

Backend Engineer

• Develop and maintain Python-based web applications and data-driven solutions
• Design and implement user interfaces with dynamic layouts, filters, tabular views, and real-time data summaries
• Build and manage RESTful APIs using frameworks such as FastAPI, Flask, or Django REST Framework
• Implement request validation, authentication, and error handling mechanisms
• Write, optimize, and manage complex SQL queries across relational and analytical databases
• Integrate applications with databases using SQLAlchemy, native drivers, or ORM tools
• Work with existing schemas, views, and data models to support application requirements
• Handle concurrent data updates, enforce validation rules, and manage controlled data writes
• Develop and maintain PySpark-based data processing workflows
• Run and monitor Spark jobs on cloud or managed data platforms
• Deploy and manage applications in Azure (experience with AWS or GCP is also acceptable)
• Configure and manage cloud services, including databases, IAM, secret management, and networking
• Implement secure authentication solutions, including SSO (Entra ID, OAuth2, SAML) and RBAC
• Containerize applications using Docker and deploy to Kubernetes environments (AKS preferred)
• Apply security best practices and design resilient, fault-tolerant systems

Python Web Developer

Data Engineer

• Design and build real-time and batch data pipelines supporting trading workflows (orders, executions, positions, market data)
• Develop low-latency data processing systems for near real-time decisioning
• Build scalable data architectures for high-volume transaction data
• Enable event-driven architectures using streaming platforms (Kafka, Kinesis)
• Integrate with trading platforms (OMS/EMS), portfolio systems, and advisor platforms
• Support use cases such as:
• Trade lifecycle tracking (order → execution → settlement)
• Portfolio performance and analytics
• Advisor dashboards and client reporting
• Ensure data consistency across front-, middle-, and back-office systems
• Build and manage data lakes / lakehouse architectures (Delta Lake, Iceberg, etc.)
• Develop ETL/ELT pipelines using modern frameworks
• Design data models optimized for trading and analytics workloads
• Implement API-driven data access layers for downstream consumption
• Optimize pipelines for low latency, high throughput, and fault tolerance
• Implement data quality, reconciliation, and observability frameworks
• Ensure high availability and disaster recovery for critical trading data systems
• Implement data governance, lineage, and auditability
• Ensure compliance with regulatory requirements (SEC, FINRA, etc.)
• Enable data security, entitlements, and access controls
• Support trade surveillance and reporting requirements
• Partner with trading desks, product teams, and architects to translate requirements into scalable data solutions
• Work closely with AI/analytics teams to enable downstream insights and models
• Mentor junior engineers and contribute to data engineering best practices

• Design and build scalable AI platforms supporting LLMs, RAG pipelines, and multi-model orchestration
• Develop reusable frameworks for prompt management, model routing, evaluation, and monitoring
• Implement LLMOps / MLOps pipelines for continuous integration, deployment, and lifecycle management
• Architect API-first AI services for enterprise-wide consumption.
• Integrate and optimize models from providers like OpenAI, Anthropic, Google DeepMind, and open-source ecosystems
• Build multi-model strategies (closed + open source) for performance, cost, and governance
• Implement advanced techniques:
• Retrieval-Augmented Generation (RAG)
• Tool use / agents
• Fine-tuning and embeddings
• Context optimization and memory systems.
• Design systems aligned with security, compliance, and data privacy requirements
• Implement guardrails, auditability, and explainability in AI workflows
• Enable safe AI deployment in distributed environments (e.g., advisor desktops, hybrid cloud).
• Build AI-driven use cases such as:
• Intelligent document processing (e.g., wealth plans, research docs)
• Advisor copilots and decision support systems
• Knowledge assistants and enterprise search
• Partner with business teams to translate use cases into scalable AI solutions.
• Develop evaluation frameworks for accuracy, hallucination detection, and model performance
• Optimize latency, throughput, and cost for production deployments
• Establish benchmarking and observability standards

Senior AI Engineer

Product Manager

• Own and manage the product backlog, ensuring alignment with business goals
• Define and prioritize features, user stories, and acceptance criteria
• Serve as the final authority on priorities and business value within Agile teams
• Participate in Agile ceremonies (sprint planning, reviews, retrospectives)
• Act as the voice of the business, advisor, and client
• Partner with stakeholders across technology, operations, compliance, and business teams
• Communicate product vision and roadmap clearly at sprint and release levels
• Drive data-informed decisions and business case development
• Improve trading functionality, order management, and trade routing workflows
• Support advisor-facing investment tools and portfolio management capabilities
• Work with vendor-integrated platforms and financial systems
• Ensure solutions align with the full lifecycle of trading and wealth management
• Define KPIs and track product performance, client satisfaction, and efficiency
• Ensure solutions meet regulatory and compliance requirements
• Continuously evaluate industry trends and fintech innovations

Senior Product Owner

Full-stack Engineer

• Lead end-to-end design and delivery of Tax Lot Harvesting capabilities
• Architect scalable solutions using .NET, AWS, and Angular-based front-end integration
• Define interaction patterns between agentic AI services and Angular UI (APIs, real-time insights, UX flows)
• Enable natural-language-driven insights surfaced through intuitive UI components
• Design systems to identify tax optimization opportunities (gain/loss harvesting, drift, cash)
• Ensure seamless embedding of intelligence into Order Entry workflows and UI
• Drive engineering best practices across backend and frontend integration
• Mentor team members across full-stack (backend + Angular) development

Lead Software Developer

• Own end-to-end lifecycle of assigned data products, including vision, strategy, roadmap, and delivery.
• Collaborate with business units, analytics teams, and technology partners to prioritize features and enhancements.
• Define and track key product success metrics and adoption KPIs.
• Advocate for data products across the organization, ensuring alignment with enterprise data governance and cloud/data strategy initiatives.
• Act as the primary liaison between business stakeholders and data engineering/analytics teams.
• Gather and translate business requirements into actionable data product specifications.
• Facilitate cross-functional collaboration to resolve trade-offs and dependencies.
• Ensure data products comply with regulatory, security, and privacy requirements.
• Define and enforce data quality standards, lineage, and observability metrics.
• Drive delivery of data products using agile methodologies.
• Promote a culture of data-driven decision-making and product-centric thinking.

Data Product Manager

• Architect and lead delivery of Agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration layers on Azure / AWS / GCP.
• Design multi-agent pipelines incorporating reasoning, planning, tool-use, memory, and feedback loops tailored to enterprise workflows.
• Partner with C-suite and senior stakeholders to translate ambiguous business challenges into well-scoped AI roadmaps and delivery plans.
• Conduct AI readiness assessments, data audits, and opportunity prioritization workshops for prospective clients.
• Define reference architectures for RAG, tool-augmented agents, AI gateways, and human-in-the-loop workflows.
• Drive responsible AI governance — including bias audits, hallucination mitigation, prompt-injection hardening, and model risk frameworks.
• Establish engineering best practices: CI/CD for ML, LLMOps, evaluation harnesses, and performance benchmarking.
• Stay at the cutting edge — evaluate emerging models (GPT-o3, Claude 3.x, Gemini Ultra, open-source LLMs) and tooling for fit-for-purpose applicability.
• Originate and expand client relationships; contribute to proposals, RFP responses, and Statements of Work.
• Build and scale the agentic AI practice — create reusable accelerators, IP assets, and delivery playbooks.
• Mentor and develop a team of AI engineers, ML scientists, and business analysts; conduct performance reviews and career development planning.
• Represent the firm at conferences, publish thought-leadership content, and contribute to the external AI community.

AI Lead – Agentic AI Consultant

• Design the comprehensive solution architecture for the FCCM-to-Actimize migration, including data migration strategy, integration topology, and phased cutover plan.
• Define integration architecture between Nice Actimize and upstream/downstream enterprise systems: core banking, data warehouses, case management, and reporting platforms.
• Establish architectural patterns for event-driven data ingestion pipelines feeding Actimize detection engines using Apache Kafka or equivalent.
• Lead architecture governance, conduct design reviews, and enforce coding and security standards.
• Oversee data model migration from Oracle FCCM schemas to Actimize data structures, ensuring fidelity of historical data and detection continuity.
• Collaborate with Nice Actimize professional services and vendor SMEs to validate platform configuration choices.
• Define and enforce NFRs: availability SLAs, latency targets, data retention policies, and disaster recovery objectives.
• Guide and mentor the Java Technical Lead and development team; perform technical risk identification and mitigation planning.
• Communicate architectural decisions and trade-offs to both technical and executive stakeholders.

Software Architect

Production Engineer

• Provide production support for .NET applications, ensuring high availability and performance.
• Monitor application health and performance using Dynatrace; analyze logs, metrics, and traces to identify root causes.
• Perform SQL DBA activities including database monitoring, performance tuning, backup/recovery, and troubleshooting.
• Collaborate with development teams to resolve application defects and optimize performance.
• Implement proactive monitoring, alerting, and automation to reduce downtime.
• Participate in incident management, root cause analysis, and post-mortem reviews.
• Document support processes, troubleshooting steps, and best practices.
• Work closely with cross-functional teams (Dev, QA, Infra, Cloud Ops) to ensure smooth production operations.

.NET Production Support Engineer

Data Analyst

• Collect, clean, and analyze financial and client data from multiple sources.
• Develop dashboards and reports to track portfolio performance, asset allocation, and risk metrics.
• Provide insights into client behavior, investment trends, and product performance.
• Support advisors with data-driven recommendations for client portfolios.
• Collaborate with investment teams to evaluate new products and strategies.
• Ensure compliance with regulatory requirements in data handling and reporting.
• Automate recurring reporting processes to improve efficiency.
• Present findings to stakeholders in clear, concise formats (visualizations, presentations, reports).

Systems Engineer

• Analyze existing monolithic systems, databases, and business logic to document "as-is" states.
• Map legacy functions to "to-be" cloud-native architectures (Microservices, APIs, Event-driven designs).
• Write high-quality User Stories with clear Acceptance Criteria (AC) that account for technical debt and platform scalability.
• Collaborate with data engineers to define mapping rules and validation logic for moving data from legacy schemas to modern structures.
• Lead Daily Stand-ups, Sprint Planning, Reviews, and Retrospectives focused on continuous improvement.
• Proactively identify and clear "modernization-specific" blockers.
• Partner with Product Owners to prioritize technical debt alongside new feature development.
• Use JIRA/Azure DevOps to track Velocity, Burndown, and Cycle Time to predict delivery timelines.

Business Systems Analyst / Scrum Master

• Design, develop, and maintain scalable C#/.NET Core microservices and RESTful APIs with a strong emphasis on clean API architecture, versioning, and performance optimization.
• Architect and implement cloud-native solutions on AWS, leveraging services such as ECS/EKS, Lambda, API Gateway, SQS/SNS, DynamoDB/RDS, S3, and CloudWatch.
• Build and evolve domain models supporting portfolio management, positions, transactions, orders, fees, performance, and regulatory reporting.
• Develop and integrate AI-enabled services, including use of LLMs, embeddings, or ML models for automation, data enrichment, anomaly detection, or decision support.
• Partner with data and analytics teams to support AI/ML pipelines, feature engineering, and intelligent data services.
• Integrate with external systems including custodians, market data providers, CRMs, and downstream analytics platforms (ETL/ELT pipelines).
• Ensure secure-by-design architecture including OAuth2/OIDC, encryption, audit logging, and PII compliance aligned with financial regulations.
• Build and maintain CI/CD pipelines using GitHub Actions, Azure DevOps, or Jenkins; implement containerization with Docker and infrastructure-as-code using Terraform or CloudFormation.
• Optimize system performance, scalability, and reliability for low-latency, high-throughput financial APIs.
• Collaborate with Product, Compliance, and Business stakeholders to deliver solutions aligned with regulatory and SLA requirements.

Senior Software Developer

Manager

• Support the day-to-day management of client engagements, ensuring deliverables are aligned to client objectives and timelines.
• Act as the coordination point between technical delivery teams and client stakeholders, facilitating clear and effective communication.
• Assist in managing project plans, risk logs, status updates, and governance routines across data engineering, analytics, and AI/ML workstreams.
• Contribute to requirement gathering, backlog grooming, and user story definition with product owners and technical leads.
• Track KPIs, usage metrics, and program health to measure impact and value realization, providing insights for continuous improvement.
• Help drive client communications, including weekly reports, executive briefings, and stakeholder alignment meetings.
• Support business development activities including pitch decks, proposal inputs, and account strategy development.
• Stay aligned with AWS best practices and relevant automotive use cases (e.g., workforce management, smart manufacturing), ensuring the application of cutting-edge solutions.

AI Engineer

Python Web Developer

Data Engineer

Senior AI Engineer

Senior Product Owner

Lead Software Developer

Data Product Manager

AI Lead – Agentic AI Consultant

AI Lead – Agentic AI Consultant

Software Architect

.NET Production Support Engineer

Data Analyst

Business Systems Analyst / Scrum Master

Senior Software Developer

Junior Engagement Manager