SQL AI Jobs

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As a Software Engineer on the Data Platform team at AKASA, you will support Machine Learning’s efforts to train in-house models via custom data pipeline development, contribute directly to product development through maintaining in-app dashboarding infrastructure, enable Analytics and Data Science efforts by developing and maintaining core Data Platform infrastructure and tooling, build new and maintain existing data pipelines to support Machine Learning model training and scalable customer deployments, work with technologies including Python, Postgres, Redshift, Prefect, Kubernetes, Grafana, and various AWS services like SQS and Cloudwatch, and explore new technologies to grow your technical skillset.

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Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help customers achieve success. Scope and co-develop production-level data science projects with customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

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Build and scale the systems that power model and product evaluations across Harvey. Embed evaluation workflows and readiness checkpoints into the product development lifecycle. Create the single source of truth for evaluation status, results, history, and launch readiness. Turn expert-designed evaluation methodologies into scalable, repeatable operational processes. Manage relationships with human data vendors and ensure evaluation quality meets legal standards. Work with Engineering and Research to improve evaluation tooling, automation, and dashboards. Drive evaluation readiness for major product and model launches across geographies and jurisdictions. Document and operationalize evaluation governance as complexity increases. Help define how Harvey ensures model accuracy, reliability, and trust at global scale.

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Own the technical architecture including backend, frontend, infrastructure, and AI integration. Write code daily to build critical systems. Make stack and design decisions shaping the product for years. Set standards for code quality, testing, and engineering discipline. Integrate large language models (LLMs) and AI workflows into the platform. Work directly with founders on product direction and the technical roadmap. Create clarity and structure in an environment lacking perfect specifications. Mentor and guide other developers technically.

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AI deployment architects are responsible for configuring, deploying, and evolving AI agents for enterprise customers by translating business requirements into robust configurations, ensuring deployments reflect client needs, and iterating rapidly to maintain high performance and system stability. They build and adapt screening flows based on customer jobs and requirements, configure state prompts, tone parameters, voice selection, transitions, and conditional logic, set up and maintain custom vocabularies for ASR, prepare and run demos, and support pilot implementations from start to finish. They analyze conversation transcripts to identify errors or drift, run isolated state tests for debugging, iterate on prompts and configurations to improve performance, use SQL for investigating behavioral patterns and validating improvements, and advise customers on screening design, personas, and best practices for AI-driven interviews. They communicate technical concepts, manage expectations during pilots, build feedback loops, and act as trusted guides throughout deployment and iteration cycles. Additionally, they surface recurring field issues for productization, contribute insights for new configuration surfaces and system capabilities, and partner with product and engineering teams to test and validate new features for scale. They help define standards, tools, and best practices for scalable agent deployment and shape playbooks and industry standards for deployment engineering in conversational AI.

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The Senior Data Scientist will own Fyxer AI’s data science capabilities, setting the roadmap for key business areas like marketing and retention, implementing scalable solutions, and ensuring stakeholders use data to make confident commercial decisions. Responsibilities include building and refining predictive models on multi-channel customer and usage data to drive product and marketing decisions, collaborating with engineering, marketing, sales, and product teams to define KPIs, experiment with new algorithms, and surface actionable insights that drive impact. They will maintain data infrastructure including BigQuery, dbt, and Fivetran, ensure data quality for reporting and self-service analytics, and develop a culture of data-driven decision making by proactively suggesting improvements to tools, processes, and architecture.

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Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help make customers successful. Scope and co-develop production-level data science projects with customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

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Design and develop scalable backend services operating in cloud environments; develop backend and algorithm features for AI-based functionalities such as price prediction, route optimization, and automation services; develop stable B2B API integration with external partners and corporate clients; analyze functional requirements and design API interface structures; continuously refactor for service performance monitoring and structural improvement; participate in code reviews and technical decision-making to enhance quality; ensure stability and high availability of services operated in cloud and container-based environments.

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As a Client Solutions Software Engineer, you will execute implementation tasks for new deployments, including requirements analysis, configuration, testing, rollout, and support of production health. You will work with client engagement teams to guide customers through technical onboarding, integration setup, troubleshooting, and data acquisition. You will contribute feedback and insights that shape product and platform improvements, and work closely with R&D engineering teams to build robust scalable client solutions.