[Hiring] Technical Consultant @SymphonyAI

Role Description

Successful client onboarding depends on reliable technical delivery, clear communication, and the ability to solve complex integration problems. As a Technical Consultant, you’ll own technical configuration, deployment, and integration for assigned client workstreams across SaaS and client-hosted environments. You’ll translate business and regulatory requirements into practical technical specifications, build and validate data pipelines, and support delivery through SIT, UAT, OAT, and production deployment. This is a hands-on, client-facing role where you’ll use logs, data, automation, and root cause analysis to keep implementations moving, make risks visible, and deliver production-ready solutions.

Responsibilities

• Own technical delivery. Lead configuration, deployment, integration, and data pipeline activities for assigned client workstreams from requirements through OAT and production.
• Translate requirements into solutions. Turn client business, regulatory, data, and security needs into clear technical specifications and implementation plans.
• Build reliable data pipelines. Develop, validate, orchestrate, and deploy SQL- and Python-based pipelines with appropriate data quality checks, scheduling, documentation, and operational readiness.
• Configure and deploy applications. Use GitHub, YAML, Helm, Kubernetes, and CI/CD practices to package, configure, and deploy solutions across SaaS and client environments.
• Solve problems at the source. Use logs and root cause analysis to resolve moderately difficult issues across data, APIs, SAML/SSO, networking, deployment, and application configuration.
• Support client acceptance. Plan and support SIT, UAT, OAT, technical reviews, validation, and production deployment while protecting delivery timelines, SLA commitments, and client acceptance.
• Put AI to work. Use AI tools, LLMs, and RAG to accelerate technical workflows, pipeline development, validation, documentation, and troubleshooting while checking outputs for accuracy and suitability.
• Integrate AI capabilities. Support model endpoints, data preparation, and AI or LLM API integration through REST endpoints or SDKs, and help monitor deployed AI solutions.
• Partner across teams. Work closely with client stakeholders, Technical Program Managers, Solution Architects, Data Science, Engineering, and Product Delivery on decisions, risks, and scope.
• Make progress visible. Provide proactive status updates on delivery, dependencies, risks, issues, and technical decisions to project sponsors and senior stakeholders.
• Help others grow. Share implementation practices and mentor junior consultants while escalating architecture changes and material scope decisions to the appropriate senior owners.

Qualifications

• 3 to 7 years of technical implementation experience with hands-on delivery in enterprise or client-facing environments, including at least one deployment through UAT or one data pipeline delivered to production.
• Strong SQL and data skills. You can build queries and pipelines, perform data validation and quality checks, and troubleshoot database or data integration issues.
• Linux/Unix and scripting experience using Bash and the ability to read and troubleshoot Python, Java, or similar code.
• Deployment and automation experience with Git, GitHub, CI/CD, YAML, Helm, and working knowledge of Kubernetes.
• Integration experience with XML and JSON configuration, REST APIs, SAML/SSO, and basic networking concepts.
• Cloud experience with at least one major platform such as AWS, Azure, or GCP.
• Strong problem-solving skills. You use logs, evidence, and root cause analysis to resolve integration, deployment, data, and environment issues.
• Client-facing communication skills. You can explain complex technical topics clearly, manage stakeholder expectations, and provide proactive updates on progress, risks, and issues.
• An ownership mindset. You can manage an assigned workstream end to end, balance multiple priorities, and escalate architecture or scope changes at the right time.
• Practical AI experience. You have used AI tools or LLM APIs to accelerate technical work and understand the importance of validating outputs before integrating them into business processes.

Requirements

• Hands-on experience with AML, KYC, fraud, sanctions, transaction monitoring, alert management, or other compliance platforms.
• Experience with Spark, Snowflake, or another enterprise data platform.
• Experience with pipeline orchestration, scheduling, production monitoring, and data quality frameworks.
• Exposure to ML model deployment, model endpoints, feature preparation, or monitoring for data drift and model degradation.
• Experience with GraphQL, microservices, rule engines, or complex enterprise integrations.
• A cloud certification in AWS, Azure, or GCP.
• Experience working with global enterprise clients and distributed delivery teams.
• Understanding of AI model governance, including explainability, auditability, and data privacy in production environments.