We are seeking a visionary and hands-on Enterprise Data Integration Architect to join our Integration Architecture team and serve as the dedicated integration representative to partner with the Data & AI organization. This role is pivotal in shaping and executing our enterprise-wide integration strategy for data and AI initiatives, leveraging cutting-edge technologies and cloud-native platforms on AWS and/or Azure.
Responsibilities
Define and drive the integration strategy for data and AI initiatives across the enterprise
Partner with Data & AI leadership to align integration architecture with business goals and data strategies
Evaluate and recommend modern integration technologies and patterns (e.g., event-driven, API-led, streaming, data mesh)
Partner with regional teams in defining and designing integration patterns and architectures
Design scalable, secure, and resilient integration architectures on AWS and/or Azure using services such as Glue, Lambda, Kinesis, S3, Redshift, and others
Requirements
Bachelor’s or Master’s degree in Computer Science
Information Systems, or related field
Nice to have: Azure cloud services
Nice to have: regulated industries (healthcare, finance)
Lead Software Engineer in Emerging Technology group responsible for designing, building, piloting, and scaling next-generation software solutions using emerging technologies.
Compensation
Payrate: $80.00 - $85.00/hr
Responsibilities
Lead architecture, design, development and testing of modern software solutions, prototypes, pilots, and reusable platform capabilities.
Evaluate, prototype, and apply emerging technologies such as generative AI, agentic workflows, retrieval-augmented generation, cloud-native services, APIs, automation, and data-driven application patterns.
Translate business opportunities, product goals, and technical requirements into scalable software designs, solution patterns, technical roadmaps, and implementation plans.
Mentor engineers, provide technical direction, facilitate design reviews, and promote a culture of experimentation, learning, engineering excellence, and responsible technology adoption.
Collaborate with product managers, architects, data scientists, cybersecurity, infrastructure, UX, and business stakeholders to deliver solutions aligned to organizational goals and risk expectations.
Forward Deployed Engineer combines hands‑on software engineering with client‑facing delivery, turning business requirements into production‑ready AI solutions.
Responsibilities
Work embedded with client and Sia teams, taking part in planning, stand‑ups, and delivery activities.
Run technical discovery with client stakeholders and turn business needs into scoped, buildable deliverables with clear acceptance criteria.
Build production‑grade Python services and APIs (FastAPI and REST) that power Generative AI applications, including RAG pipelines over enterprise knowledge sources.
Integrate LLM APIs and SDKs into client environments, handling authentication, rate limits, fallbacks, latency, and cost.
Connect solutions to enterprise systems such as databases, internal APIs, document stores, and business applications.
Requirements
Bachelor's or Master's degree in Computer Science, Engineering or a related field, or equivalent practical experience.
Nice to have: Agent orchestration and tool‑calling frameworks (LangGraph, LangChain, OpenAI or Claude SDKs)
Nice to have: Model Context Protocol (MCP)
Nice to have: Enterprise GenAI platforms and managed AI services on AWS Azure or GCP
Support the operations and/or manage delivery for production systems and services based on operational requirements and service agreement.
Responsibilities
Own reliability of observability platforms—Splunk—defining and maintaining SLIs, SLOs, and error budgets for metrics pipelines, alerting systems, and dashboard availability across all infrastructure tiers
Engineer auto‑remediation for common observability failures—scraper restarts, index rollover failures, ingest pipeline blockages—reducing MTTR and eliminating repetitive manual toil
Implement and govern OpenTelemetry instrumentation standards across the infrastructure estate—ensuring telemetry coverage is comprehensive, consistent, and production‑grade
Drive observability‑as‑code adoption—dashboards, alert rules, SLO definitions, and recording rules version‑controlled and deployed through GitOps pipelines with automated testing
Perform capacity planning and performance analysis for observability platforms—managing cardinality growth, storage retention, query performance, and ingest throughput at scale
Build, run and maintain the ML infrastructure behind Kavak UAE's pricing, including mapping of market listings to catalog SKUs, pricing algorithms, and other ML models to improve pricing accuracy, strategy and margin.
Responsibilities
Build and maintain the listing-to-SKU mapping engine
Develop an image-based car identification model
Manage the local pricing algorithm, monitor performance, run experiments, improve accuracy and reduce bias
Design backtests, holdout sets and audit samples to measure accuracy
Productionise models as scheduled pipelines with monitoring, retraining and data quality checks
Requirements
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related field
Nice to have: Google Cloud Professional Machine Learning Engineer
Nice to have: AWS Certified Machine Learning Engineer - Associate
A senior individual-contributor leadership role defining and driving the architecture of AI and agentic systems across multiple teams and product domains, turning experimental AI capabilities into reliable production systems.
Locations (2)
Boca Raton, Florida, United States
Hyderabad, Telangana, India
Responsibilities
Define and drive technical direction for AI and agentic systems, and contribute to the AI platform roadmap across teams
Influence architecture decisions for compute, cloud, and AI infrastructure across teams
Lead the design of large-scale AI/LLM systems: inference platforms, APIs, and distributed architectures
Architect production multi-agent systems end-to-end: orchestration, state management, tool integration, and failure handling
Define and drive best practices and standards for AI/LLM systems across teams (agent design, evaluation, observability, reliability)
Design, implement, and operate globally distributed systems for Amazon Leo’s low Earth orbit satellite network, enabling low-latency, AI-driven internet connectivity.
Compensation
USA, WA, Redmond - 168,100.00 - 227,400.00 USD annually
Responsibilities
Architect and implement scalable, cost-optimized S3-based Data Lakehouse
Architect and implement scalable, cost-performance-optimized OLAP-based analytics layer
Establish metadata management with automated data classification and lineage tracking
Design and enforce standardized data ingestion patterns with built-in quality controls and validation gates
Architect a centralized metrics repository
Requirements
Bachelor's degree in computer science
Nice to have: full software development life cycle
Design enterprise-grade AI solutions and bridge business strategy, enterprise architecture and technology innovation.
Responsibilities
Define end-to-end enterprise AI architectures aligned with business strategy, enterprise architecture and technology innovation.
Design AI solution architectures spanning Generative AI, LLMs, Agentic AI, RAG, Machine Learning, Data Engineering and cloud platforms.
Translate business requirements into architecture blueprints and reusable patterns covering APIs, integration, data flows and supporting infrastructure.
Guide AI initiatives from proof of concept through production to scale, ensuring architectural consistency across the AI/LLM lifecycle.
Design scalable, resilient and secure AI architectures, including APIs, integration and supporting cloud infrastructure.
Requirements
Bachelor's degree in Computer Science, Information Technology, or a related STEM field
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