Jobs at Aligned Automation

Leads customer-facing process excellence and business process management engagements across discovery, modeling, process mining, redesign, automation, implementation, and benefits realization. The role identifies inefficiencies and root causes, develops transformation roadmaps and business cases, manages multidisciplinary teams, establishes governance, and delivers measurable operational improvements. It also integrates BPM, RPA, analytics, GenAI, and agentic AI opportunities across complex enterprise processes.

Analyze and optimize enterprise business processes using BPM, process mining, and task mining. Identify bottlenecks, deviations, root causes, and automation opportunities; create process maps, dashboards, reports, and recommendations. Support RPA and AI-enabled improvement initiatives through requirements documentation, testing, KPI monitoring, and benefits tracking. Collaborate with consultants, analysts, automation engineers, AI teams, and customer stakeholders to implement continuous improvement initiatives.

Develop and maintain Power BI dashboards, reports, and data models. Translate business requirements into scalable BI solutions using DAX and Power Query, ensure data accuracy and performance, troubleshoot reporting issues, and deliver actionable insights. Collaborate with stakeholders and cross-functional teams while applying data visualization, governance, and development best practices. SQL, cloud data platforms, data integration tools, and exposure to Agentic AI or Generative AI are advantageous.

Manage end-to-end enterprise transformation programs spanning Celonis process mining, data and analytics, AI/ML, and operational excellence. Own governance, delivery cadence, scope, dependencies, risks, resources, vendors, KPIs, benefits realization, and executive communications. Facilitate stakeholder alignment across leadership, business, technology, compliance, and partners while tracking delivery health and enabling measurable business value.

Build and maintain scalable full-stack applications using Python, FastAPI, React, PostgreSQL, RabbitMQ, Redis, Docker, and Kubernetes. Develop RESTful APIs, optimize databases and application performance, maintain CI/CD pipelines, and integrate LLM-powered features through prompt engineering. Collaborate across teams to design, test, deploy, and support reliable software solutions.

Provides technical support for enterprise servers, storage, client, and consumer systems. Responsibilities include managing tickets, coordinating field-service dispatches and replacement parts, responding to customer emails, documenting resolutions, troubleshooting hardware and operating-system issues, meeting SLA targets, and escalating complex cases. The role supports global customers through rotational shifts and includes training on products, tools, and technologies.

Leads L1 and L2 technical support operations across global rotational shifts. Manages ticket queues, dispatch coordination, SLA compliance, escalations, staffing, rosters, performance, quality audits, governance, reporting, and process improvement. Oversees enterprise hardware support for servers, storage, tape libraries, and client systems, while coordinating with field services, engineering, product teams, clients, and stakeholders.

Design and deliver production GenAI and agentic applications, including RAG systems, tool-using assistants, and multi-agent workflows. Own model, retrieval, orchestration, serving, evaluation, observability, and guardrail architecture. Translate business needs into high-value AI solutions, establish technical standards, review code and designs, and mentor data scientists and engineers. The role also requires production engineering, optimization, stakeholder collaboration, and staying current with emerging AI technologies.

Leads solution architecture for a complex, multi-phase technology programme, serving as the primary technical authority. Owns high- and low-level design, architecture governance, technical decisions, risk mitigation, compliance, validation, testing strategy, and quality assurance. Participates in Agile ceremonies and governance forums, provides evidence-based recommendations to senior stakeholders, produces architecture and compliance documentation, and mentors project team members. Requires substantial client-facing architecture experience and the ability to operate independently in regulated or mission-critical environments.

Leads solution architecture for a complex, multi-phase client technology programme. Serves as the primary technical authority, owns high- and low-level design, evaluates architectural risks and trade-offs, governs technical decisions, and ensures compliance with standards and regulatory requirements. Participates in Agile delivery, oversees validation and quality practices, approves architecture and compliance documentation, represents architecture in governance forums, and mentors project team members.

Owns the architecture and production delivery of enterprise data and AI solutions. Responsibilities include building data pipelines, governance integrations, semantic layers, AI agents, LLM applications, and readiness frameworks; applying technologies such as LangGraph, LangChain, RAG, and DataRobot; improving performance and scalability; and leading, mentoring, and managing a technical delivery team while engaging enterprise clients.

Business Analyst focused on data engineering and analytics, responsible for requirements gathering, stakeholder management, SQL analysis, data profiling, quality validation, reconciliation, reporting, and documentation. The role requires understanding ETL/ELT, pipelines, data warehousing, data modeling, databases, and Agile delivery. Experience with Python, PySpark, Airflow, cloud data platforms, APIs, and AI/ML data use cases is advantageous.

Leads enterprise data management, modernization, governance, and AI transformation initiatives. The role architects scalable cloud data platforms and AI-ready data products, including GenAI and agentic AI solutions using LLMs, RAG, vector databases, knowledge graphs, and semantic layers. Responsibilities include executive client engagement, consulting, solution architecture, program and team leadership, delivery governance, financial performance, pre-sales, account growth, and mentoring senior data and AI professionals.

Leads enterprise Data and AI consulting, solution architecture, presales, and delivery. Designs data management, cloud modernization, governance, analytics, GenAI, and agentic AI solutions using modern data and semantic technologies. Develops proposals, RFP/RFI responses, SOWs, estimates, roadmaps, business cases, and reusable accelerators. Conducts executive client workshops, guides implementation teams, and ensures scalable solutions deliver measurable business value and ROI.

Lead enterprise AI and machine learning strategy, architecture, development, and production deployment. Design predictive analytics, generative AI, LLM, RAG, and agent solutions; establish MLOps, governance, monitoring, and responsible AI practices; align initiatives with business value and ROI; communicate with executives and stakeholders; prioritize and deliver AI programs; and mentor data scientists, ML engineers, and analysts.

Deliver end-to-end digital transformation and AI-enabled solutions by gathering requirements, designing functional solutions, coordinating cross-functional teams, executing SDLC activities, supporting testing and deployment, and driving business adoption. Use analytics, automation, and AI where applicable and maintain governance, documentation, and continuous improvement.

Lead end-to-end delivery of digital transformation and AI initiatives, establish project governance, manage resources and stakeholders, drive Agile execution, mentor consultants, and ensure on-time, high-quality business outcomes and adoption.

Design, build, and optimize scalable ETL/ELT data pipelines using PySpark and Python. Model and tune MongoDB for large datasets, develop data ingestion and validation frameworks, integrate REST APIs, monitor and troubleshoot pipelines, and collaborate with analysts and data scientists while applying data governance and CI/CD practices.

Design, develop, and deploy enterprise GenAI and Agentic AI solutions. Build multi-agent workflows, RAG systems, vector database integrations, scalable Python APIs, and microservices. Implement LLM orchestration, prompt engineering, evaluation, monitoring, and observability. Integrate AI with enterprise applications and data platforms while meeting security, governance, SDLC, and deployment standards. Collaborate with stakeholders to convert business use cases into production-ready systems.

Design, develop, and deploy enterprise-scale GenAI and agentic AI solutions. Build multi-agent workflows and RAG systems using vector databases and enterprise knowledge sources. Develop Python APIs and microservices, implement LLM orchestration, prompt engineering, evaluation, monitoring, and observability, and integrate AI with enterprise applications and data platforms. Collaborate with stakeholders to deliver production-ready systems while following software development, security, governance, and deployment standards.