Technical Consultant

SymphonyAI•UNAVAILABLE, UNAVAILABLE

3d•Remote

About The Position

As a Technical Consultant at SymphonyAI Financial Services, you will own technical configuration, deployment, and integration for assigned client workstreams across SaaS and client-hosted environments. You will translate business and regulatory requirements into technical specifications, build and validate data pipelines, and support delivery through system integration testing (SIT), user acceptance testing (UAT), operational acceptance testing (OAT), and production deployment. This is a hands-on, client-facing role working with client compliance, risk, and technology teams, as well as Technical Program Managers, Data Science, Engineering, Solution Architects, and Product Delivery. You will use logs, data, automation, and root cause analysis to address integration problems, surface risks, and deliver production-ready solutions. The role also involves applying AI tools and LLMs to technical workflows and supporting the integration and monitoring of AI capabilities.

Requirements

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, including building queries and pipelines, performing data validation and quality checks, and troubleshooting database or data integration issues.
Linux/Unix and Bash scripting experience, with the ability to read and troubleshoot Python, Java, or similar code.
Deployment and automation experience with Git, GitHub, CI/CD, YAML, and Helm, plus 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.
Problem-solving skills using logs, evidence, and root cause analysis to resolve integration, deployment, data, and environment issues.
Client-facing communication skills, including explaining complex technical topics, managing stakeholder expectations, and providing proactive updates on progress, risks, and issues.
Ability to manage an assigned workstream end to end, balance multiple priorities, and escalate architecture or scope changes appropriately.
Practical experience using AI tools or LLM APIs to accelerate technical work, with an understanding of the need to validate outputs before integrating them into business processes.

Nice To Haves

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.

Responsibilities

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