• We are looking for more experienced (above 12 years) and played lead / architect roles for this position.
• We are seeking a Senior Engineer to own and evolve a production-critical payment platform responsible for high-volume financial transactions.
• This role is focused on stability, correctness, and long-term system stewardship, not greenfield development.
• In addition to strong programming expertise, this role requires experience embedding AI/ML capabilities into enterprise systems and SDLC processes, supporting AI-driven transformation initiatives such as intelligent automation, anomaly detection, and data-driven decisioning within transactional platforms.
• The ideal candidate has deep experience operating Tier 1 systems where reliability, data integrity, and performance are non-negotiable.
• They demonstrate strong engineering judgment, maintain a disciplined and low-complexity approach to design, and are capable of driving meaningful improvements independently within an existing distributed system.
• This role requires a hands-on engineer who can lead through execution, make sound technical decisions under real-world constraints, and contribute to the long-term evolution of a high-value platform, including selective adoption of AI/ML capabilities where they improve system outcomes without compromising stability.

Role Context The system is a Go-based, microservice-oriented payment platform running in a containerized environment with a MySQL backend. It supports high-throughput transaction processing and significant annual revenue, making reliability and correctness critical. The platform originated externally and requires thoughtful, incremental improvement rather than wholesale redesign. The work involves ongoing evolution of the system, including improving data fidelity, observability, performance, and flexibility while maintaining strict operational stability. The platform is progressively incorporating AI/ML-driven capabilities, requiring careful integration into existing system boundaries and SDLC practices., Own and evolve existing Go-based services, making measured, low-risk changes that improve system capability, reliability, and clarity. Design and implement backend functionality for payment processing, transaction routing, reconciliation, and financial data movement with a strong focus on correctness and auditability. Integrate AI/ML components into the existing architecture (e.g., model inference services, decision engines, data pipelines) while ensuring system stability and auditability. Apply AI/ML across the SDLC, including: o Intelligent test generation and validation o Production anomaly detection and incident prediction o Log and metric analysis using ML techniques o Code quality and defect prediction tools Collaborate with data science teams to operationalize ML models (deployment, monitoring, versioning, rollback strategies) within a production-grade environment. Proactively identify system limitations, operational risks, and data gaps, and drive pragmatic solutions with minimal oversight. Evaluate the impact and risk of changes in a high-throughput transactional environment, ensuring safe and controlled rollouts. Improve system observability, data capture, and operational insight to support both engineering and business needs, including ML-driven observability enhancements. Model and optimize transactional data using relational databases, with careful attention to consistency, performance, and maintainability. Build and maintain APIs and service interfaces with clear contracts and long-term usability in mind. Contribute to AI transformation initiatives, identifying where AI/ML can meaningfully enhance system reliability, fraud detection, operational efficiency, or decision-making. Collaborate with product, architecture, and non-technical stakeholders to ensure solutions meet business, operational, regulatory, and ethical AI governance requirements. Provide technical leadership by guiding design decisions, reviewing code, and mentoring engineers while maintaining a focus on simplicity and stability. Own services through their full lifecycle, including design, deployment, monitoring, incident response, and continuous improvement, including ML lifecycle management (MLOps). Mandatory Skills: GO/Golang, MySQL/Oracle/PostgreSQL, AL/ML, AWS Optional Skills: Java, Python, Kubernetes, Docker, Performs tests in strict compliance with detailed instructions for the following:

1. Ensure that new or revised components or systems perform to expectation.

2. Ensure meeting of standards; including usability, performance, reliability or compatibility.

3. Document Test results and report defects Typical performance measures:

4. Timely completion of all tasks

5. of test cases/script executed in comparison to the benchmarks

6. of valid defects

Performance Areas: Test Design, Development, Execution: Execute test cases / scripts Identify, log and track defects Retest Log in productivity data Requirements Management: Participate, Seek Clarification, Understand, Review Domain relevance: Test features and components with good understanding of the business problem being addressed for the client Manage knowledge: Consume, Contribute, + $100,000-225,000 per year Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. W…

• 1 month ago

12 plus years of experience in software engineering, demonstrated experience building and operating production-critical backend systems with meaningful business or revenue impact. Strong hands-on experience with Go/Golang or Java based technologies in real- world production environments. Experience integrating AI/ML solutions into enterprise systems, including deploying, consuming, or operationalizing ML models. Understanding of AI/ML concepts such as supervised/unsupervised learning, model evaluation, inference pipelines, and data quality considerations. Experience applying AI/ML within SDLC processes (e.g., testing, monitoring, observability, or developer productivity tooling). Deep experience with relational databases (e.g., MySQL, PostgreSQL, Oracle), including schema design, transactional modeling, and performance optimization. Strong understanding of transactional systems, including data integrity, idempotency, reconciliation, auditability, and failure handling. Proven ability to make pragmatic, low-complexity design decisions in distributed systems. Experience working within and improving existing systems with real-world constraints, rather than primarily greenfield environments. Ability to independently identify problems, define solutions, and drive execution to completion. Strong communication skills, including the ability to work effectively with both technical and non-technical stakeholders. Preferred Qualifications Experience with payment systems, financial transaction platforms, or other high- integrity data domains. Experience supporting systems with high transaction volume, low latency requirements, or strict uptime expectations. Familiarity with MLOps practices, including model deployment, monitoring, drift detection, and lifecycle management. Experience with real-time or near real-time ML inference systems in production environments. Familiarity with secure data handling, privacy considerations, and ethical AI practices in regulated environments. Experience with containerized or cloud-based deployment environments (e.g., Kubernetes, Docker, or similar platforms). Exposure to event-driven or asynchronous processing patterns. Ideal Candidate Profile Operates as a true owner, not a task executor; identifies issues and drives solutions independently. Demonstrates restraint and discipline in system design, avoiding unnecessary abstraction and complexity. Comfortable working in high-stakes environments where stability and correctness take precedence over novelty. Able to apply AI/ML pragmatically, focusing on measurable improvements rather than experimentation for its own sake. Calm, methodical, and reliable under production pressure. Able to collaborate effectively with senior peers while mentoring and guiding junior engineers. Focused on building systems that are understandable, maintainable, resilient, and progressively intelligent over time., 1. Ability to review user story / requirements to identify ambiguities

2. Ability to design test cases / scripts as per user story / requirements

3. Ability to apply techniques to design efficient test cases / script

4. Ability to set up test data and execute tests

5. Ability to identify anomalies and detail them Knowledge Examples:

6. Knowledge of Testing Methodologies

7. Knowledge of Tools

8. Knowledge of Types of testing

9. Knowledge of Testing Processes

10. Knowledge of Testing Standards