project engineer

site engineer

estimation engineer

planning engineer

devops engineer

ai platform engineer

Tata Consultancy Services

<div>
 
 
 Job Title
 
 &ndash; AI Platform Engineer
 
 
 
 Company
 
 &ndash; TCS (MEA)
 
 
 
 Location
 
 &ndash; Dubai
 
 
 
 Job type
 
 &ndash; Full time
 
 
 
 
 
 
 About Us:
 
 
 
 Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization that has been partnering with many of the world&rsquo;s largest businesses in their transformation journeys for over 50 years. TCS offers a consulting-led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions. This is delivered through its unique Location Independent Agile&trade; delivery model, recognized as a benchmark of excellence in software development.
 
 
 A part of the Tata group, India's largest multinational business group, TCS has over 616,171 of the world&rsquo;s best-trained consultants with 157 nationalities in 53 countries. For more information, visit www.tcs.com and follow TCS news at @TCS_News.
 
 
 
 
 
 
 
 Job Description:
 
 
 
 
 
 
 
 
 Key Accountabilities:
 
 
 
 
 AI Platform Operations
 
 
 
 Provide day-to-day monitoring and operational support of Gernas, its dependent AI applications, deployed AI agents, agent runtime environments, AI gateways, and supporting Azure and AWS cloud services. Ensure continuous availability and consistent service performance across production environments, proactively identifying degradation signals before they become user-visible incidents. Maintain operational dashboards covering platform health, agent execution, LLM consumption, gateway throughput, and integration endpoints, and ensure that ownership of every production component is unambiguous and documented.
 
 
 
 Incident and Problem Management
 
 
 
 Lead L2 and L3 incident response across the Gernas platform and its surrounding AI products. Conduct structured root-cause analysis, drive service restoration within agreed P1&ndash;P4 service-level commitments, and coordinate major incidents involving multiple engineering, cloud, security, and vendor stakeholders. Maintain problem records for recurring or systemic issues, ensure that post-incident reviews are completed with clear corrective and preventive actions, and own the closure of those actions through to engineering remediation. Act as the bridge between the L1 AI Operations Centre and L3 engineering teams during complex incidents.
 
 
 
 AI Agent and Workflow Support
 
 
 
 Troubleshoot the behaviour of single-agent and multi-agent workflows, including agent orchestration, tool invocation, skill execution, prompt construction, memory and context handling, planning loops, and inter-agent coordination. Diagnose failures across Microsoft Agent Framework and LangGraph execution, including state-graph traversal, conditional routing, tool-call mismatches, retries, and human-in-the-loop checkpoints. Investigate integrations with enterprise systems, MCP-exposed tools, and downstream APIs, and work with engineering teams to harden agent designs where production data reveals weaknesses.
 
 
 
 LLM and Model Service Support
 
 
 
 Support the operational stability of the model-serving layer across Azure OpenAI PTU deployments, AWS Bedrock, and Core42 Compass. Monitor and troubleshoot PTU capacity utilisation, token consumption patterns, quota and throttling behaviour, latency profiles, content filtering outcomes, and model endpoint availability. Coordinate with the AI Hub team on routing decisions between frontier and in-region models, assess cost and capacity headroom, and manage capacity escalations and vendor tickets where service degradation originates upstream.
 
 
 
 AKS and Runtime Support
 
 
 
 Provide hands-on operational support of the Azure Kubernetes Service estate hosting Gernas agent runtimes and supporting services. Troubleshoot pod, deployment, service, ingress, namespace, secret, and configuration issues; investigate autoscaling behaviour and resource utilisation anomalies; and analyse container logs, network connectivity, and runtime failures. Work closely with the Cloud Platform team on cluster-level concerns, image security, node health, and platform upgrades, ensuring AKS-hosted workloads remain stable through patching and lifecycle events.
 
 
 
 API, AI Gateway and MCP Support
 
 
 
 Operate and troubleshoot the Azure API Management layer that serves as both the enterprise API gateway and the MCP Gateway for Gernas. Diagnose issues with API policies, authentication, authorisation, routing, rate limiting, quotas, caching, and backend connectivity. Provide deep support for MCP servers, MCP tools, and the MCP Gateway pattern &mdash; including tool discovery, schema validation, protocol-level failures, and the coordination between MCP clients hosted in agents and the underlying tool endpoints. Ensure that the gateway remains a secure, observable, and policy-compliant control plane for all AI traffic.
 
 
 
 Observability and Performance Management
 
 
 
 Use Comet Opik as the primary observability surface for agent and LLM execution, working with traces, prompts, agent execution paths, latency breakdowns, token usage, errors, and model quality indicators. Build and maintain operational dashboards, alert rules, and correlation views that combine Opik telemetry with Azure Monitor, Application Insights, Log Analytics, and CloudWatch data. Lead performance optimisation initiatives where trace evidence shows hotspots in prompts, tools, retrieval steps, or model selection, and ensure that observability coverage keeps pace with platform evolution.
 
 
 
 Voice AI Support
 
 
 
 Support the production operation of ElevenLabs-based voice AI capabilities, including speech generation, voice-agent connectivity, real-time audio session handling, and API consumption patterns. Investigate latency, audio quality, dropped sessions, and integration failures across the voice channel and its dependent platforms, and coordinate with ElevenLabs and integration partners on upstream issues.
 
 
 
 Release and Change Management
 
 
 
 Validate releases prior to and immediately following deployment, exercising production verification scripts, smoke tests, and rollback procedures. Maintain release readiness through clear configuration management, environment parity checks, and pre-deployment risk reviews. Operate within FAB's change-management framework, ensuring that all production changes including patches, upgrades, configuration adjustments, and model or prompt rotations pass through the appropriate change controls and post-implementation review.
 
 
 
 Security, Risk and Compliance
 
 
 
 Uphold the security and compliance posture of Gernas and dependent AI products. Manage identity and access controls, secrets, certificates, and managed identities across the platform; coordinate vulnerability remediation and patching cycles; and maintain audit evidence for internal audit, supervisory reviews, and external assurance. Operate responsible-AI controls including content filtering, PII and PCI detection, data egress controls, and model-access governance and ensure that secure integration patterns are followed across every API, MCP tool, and external dependency.
 
 
 
 Service Improvement and Automation
 
 
 
 Drive continuous reduction of manual support effort through automation of routine operational tasks, self-healing patterns, monitoring enhancements, and proactive remediation. Maintain a current and high-quality library of support playbooks, runbooks, knowledge articles, and standard operating procedures. Identify and lead service-improvement initiatives that lift platform reliability metrics, reduce incident volume, and shorten mean time to resolution.
 
 
 
 Stakeholder and Vendor Coordination
 
 
 
 Operate as a credible technical counterpart to business units, engineering teams, the Cloud Platform team, Cybersecurity, Architecture, AI Governance, and Service Management. Lead vendor engagement with Microsoft, AWS, Core42, ElevenLabs, and other technology partners on incidents, capacity reviews, roadmap items, and product issues, ensuring that vendor accountability is exercised and that escalations are progressed effectively.
 
 
 
 On-Call and Operational Readiness
 
 
 
 Participate in a 24&times;7 support model, including a structured on-call rotation, major incident leadership, disaster-recovery exercises, business-continuity testing, and production-readiness assessments for new agents, models, and integrations entering the platform. Treat operational readiness as a release gate rather than an afterthought, and ensure that nothing reaches production without explicit operational sign-off.
 
 
 
 
 
 
 TECHNICAL SKILLS: minimum 5-7 yrs of working experience mandatory
 
 
 
 
 Cloud AI Services
 
 
 
 
 
 
 
 Agentic AI Frameworks
 
 
 
 
 LLM Operations
 
 
 
 
 Kubernetes and Containers
 
 
 
 
 API and MCP Gateway
 
 
 
 
 Observability
 
 
 
 
 DevOps and Automation
 
 
 
 
 Voice AI
 
 
 
 
 Security and Governance
 
 
 
 
 Service Management
 
 
 
 
 
 
 
 KNOWLEDGE, SKILLS, &amp; EXPERIENCE
 
 
 
 
 Education
 
 
 
 Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a closely related discipline. A relevant master's degree in AI, Machine Learning, or Cloud Computing will be considered an advantage.
 
 
 
 Professional Experience
 
 
 
 Approximately 5&ndash;7 years of overall IT experience, with significant time spent in cloud application support, platform engineering, DevOps, Site Reliability Engineering, production operations, or senior technical support roles. Of this, a minimum of 2&ndash;3 years of relevant experience supporting AI, machine learning, Generative AI, conversational AI, cloud-native platforms, or other data-intensive enterprise systems is required.
 
 
 
 Banking and Regulated Environment
 
 
 
 
 
 
 
 Technical Troubleshooting
 
 
 
 
 
 
 
 Operational Skills
 
 
 
 
 
 
 
 Communication and Leadership
 
 
 
 
 
 
 
 
 
 Thank you for your interest in applying for this position with TCS. We will review your application and will get back to you if we are considering your interest in this opportunity.
 
 
 
 
 
 
 Application Deadline: 30- July -2026
 
 
 
 
 
 
 
 Privacy Note:
 
 
 
 https://www.tcs.com/connect-with-tcs/privacy-policy
 
</div>

AI Platform Engineer

Times World Information Technology

<div>We are looking for a Senior Data &amp; AI Platform Engineer with strong expertise in data architecture, database design, and scalable system development. The ideal candidate should have a deep understanding of data engineering concepts, modern data platforms, and the ability to design robust, scalable back-end systems and data pipelines.
<div><h4 class="jobSectionHeader">Key responsibilities:</h4>
<ul><li>Design and develop scalable back-end systems</li><li>
Define and implement data architecture and database design strategies</li><li>
Build and optimize data ingestion and transformation pipelines
</li><li>Develop and maintain unified data models across multiple systems
</li><li>Design end-to-end data flows and integration strategies
</li><li>Collaborate with cross-functional teams to ensure efficient data handling and processing
</li><li>Ensure system scalability, performance, and reliability</li></ul><h4 class="jobSectionHeader">
Machine Learning &amp; AI Integration</h4><ul><li>
Work closely with the AI/ML team to understand data requirements for model training, feature engineering, and pipeline development
</li><li>Prepare, validate, and deliver clean, structured data to support AI/ML systems and intelligent workflows
</li><li>Consume and integrate outputs from ML models into data pipelines and backend systems
</li><li>Maintain awareness of ML concepts to effectively collaborate with AI/analytics teams</li></ul><h4 class="jobSectionHeader">
Core Technical Skills</h4><ul><li>
Back-end &amp; System Design</li><li>
Expertise in system scalability and performance optimization
</li><li>Solid understanding of distributed systems and back-end architecture
</li><li>Data Engineering Concepts (IMPORTANT)</li><li>
Strong knowledge of data modelling (OLTP vs OLAP)
</li><li>Strong knowledge of data lake architecture (primary) and data warehousing concepts</li><li>
Strong knowledge of REST APIs and API design (FastAPI / Flask / Django)</li></ul><h4 class="jobSectionHeader">
Core Platforms &amp; Technologies (Required)</h4>
Strong working knowledge of:<ul><li>
 Relational databases - strong hands-on experience required (e.g. PostgreSQL, MS SQL Server, MySQL)</li><li>
Non-relational / NoSQL databases - working knowledge required (e.g. MongoDB, SQLite, TimescaleDB, Redis)</li><li>
Graph databases - understanding of graph data models (e.g. Apache AGE, Neo4j concepts)</li><li>
Comfortable working across multi-database environments - relational, time-series, graph, and cache layers in a single architecture</li></ul>
Good to Have: Hands-on experience with modern data tools such as Kafka, Airbyte, S3/ADLS, Snowflake, or similar platforms<h4 class="jobSectionHeader">
Data Ingestion &amp; Processing</h4>
Ability to design and implement:<ul><li>
Data ingestion pipelines
</li><li>Data transformation layers (ETL/ELT)
</li><li>Unified and scalable data models
</li><li>System Thinking &amp; Architecture</li></ul>
Ability to:<ul><li>
Design scalable back-end architectures
</li><li>Define and manage data flow across systems
</li><li>Guide integration strategies between multiple services and platforms</li></ul>
Good to Have<ul><li>
Experience with cloud platforms (AWS / GCP / Azure)
</li><li>Exposure to real-time data processing systems
</li><li>Knowledge of ML data pipelines or analytics workflows
</li><li>Experience using Python for data ingestion pipelines, transformation scripts, and automation workflows is an advantage</li></ul>
What We&rsquo;re Looking For<ul><li>
Passion for experimenting with new tools, frameworks, and ideas
</li><li>Ability to quickly prototype and iterate on features
</li><li>Interest in blending creativity with engineering
</li><li>Comfort working in less rigid, exploratory development environments
</li><li>Strong curiosity and self-learning mindset</li></ul>
Bonus Points If You<ul><li>
Use AI-assisted coding tools (like GitHub Copilot, Cursor, Antigravity, Claude)
</li><li>Build side projects, prototypes, or experimental apps
</li><li>Enjoy rapid MVP development, or creative coding
</li><li>Stay updated with latest trends in AI, and developer tools</li></ul> 

</div><div><h4 class="jobSectionHeader">Requirements (Qualifications/Experience/Competencies)
</h4>Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, or a related field. A Master's degree in a relevant field is an advantage 
 
5 - 7 years of experience
Ready to Join Us? 
 
If you are passionate about building modern AI-powered products and enjoy working on innovative digital solutions, we would love to hear from you. Immediate joiners are preferred. 
 
Please note: This position is open to candidates currently residing in the UAE only.</div></div>

Senior Data & AI Platform Engineer

Deeplight AI

<div>
 DeepLight AI is a specialist AI and data consultancy with extensive experience implementing intelligent enterprise systems across multiple industries, with particular depth in financial services and banking. Our team combines deep expertise in data science, statistical modeling, AI/ML technologies, workflow automation, and systems integration with a practical understanding of complex business operations.
 
 
 At DeepLight, we don't believe in "off-the-shelf" fixes. We deliver tailored AI solutions designed to integrate seamlessly into existing enterprise architectures, ensuring that innovation is both scalable and secure. From building robust data foundations to deploying sophisticated AI platforms, we empower our clients to lead in an increasingly automated world.
 
 
 The DevOps Engineer owns the Agentic Workflow Automation (AWA) platform's deployment architecture, infrastructure-as-code (IaC), CI/CD pipelines, and continuous operational reliability. Operating within the AI Centre of Excellence (AICoE) at a Mid-level capacity, this position serves as the primary guardian of the cloud-native infrastructure within the bank's Azure UAE environment. The role ensures that every critical platform component&mdash;ranging from AKS namespaces and KEDA autoscalers to ADLS Gen2 storage lifecycles and firewall configurations&mdash;is entirely automated, secure, and reproducible strictly from code, eliminating manual interventions.
 
 
 
 Your responsibilities as the DevOps Engineer include:
 
 
 
 <ul>
 <li>
 Maintaining and governing all AWA Azure infrastructure utilizing Terraform or Bicep, covering multi-namespace AKS clusters, ADLS Gen2 accounts with WORM policies, API Management configurations, private endpoints, and Key Vault instances
 </li>
 <li>
 Designing and optimising automated Azure DevOps pipelines managing the full lifecycle from container compilation, ACR pushing, and Notary v2 image signing, through to managed infrastructure deployments and validation testing gates
 </li>
 <li>
 Managing multi-namespace AKS clusters, enforcing strict network policies, pod isolation, workload identities, and KEDA autoscaling rules based on active message queues or consumer lag
 </li>
 <li>
 Overseeing container registries, enforce mandatory image signing, monitor vulnerability tracking outputs, and maintain admission control validation policies via OPA Gatekeeper
 </li>
 <li>
 Administering access architectures, managed identity configurations, automated TLS certificate lifecycles, and cryptographic secret rotation schedules within Azure Key Vault
 </li>
 <li>
 Constructing complex KQL queries and alerting conditions within Azure Monitor and Log Analytics to track service-level SLA breaches, runtime failures, and token utilization thresholds
 </li>
 <li>
 Implementing platform security configurations, managing private connectivity endpoints, Azure Policy enforcement rules, and enterprise firewall architectures
 </li>
 <li>
 Defining, implementing, and validating automated disaster recovery failover paths, maintaining target Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO) for storage accounts and model serving endpoints
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 We need you to have:
 
 
 <ul>
 <li>
 A deep competence in building enterprise-grade, production-ready cloud architectures using infrastructure-as-code platforms (Terraform or Bicep)
 </li>
 <li>
 Technical mastery over Kubernetes (AKS) configuration, including Helm/Kustomize deployment engines, network policies, and cluster admission controls
 </li>
 <li>
 Practical ability to implement automated horizontal scaling strategies using Kubernetes Event-Driven Autoscaling (KEDA) or equivalent streaming metrics
 </li>
 <li>
 The capability to manage multi-stage, gated deployment pipelines across multi-environment setups using Azure DevOps or GitHub Actions
 </li>
 <li>
 A solid understanding of enterprise network routing, including hub-and-spoke VNets, private link endpoints, network security groups, and cloud firewall boundaries
 </li>
 <li>
 A minimum of 5 years of experience in dedicated DevOps, SRE, or Platform Engineering roles, with at least 2 years explicitly focused on Azure ecosystems
 </li>
 <li>
 A documented history managing production Kubernetes deployments, implementing automated RBAC, policy layers, and namespace segmentation
 </li>
 <li>
 Experience writing advanced KQL (Kusto Query Language) expressions to parse logs and feed operational dashboards
 </li>
 <li>
 Hold a Bachelor's degree in Computer Science, Systems Engineering, or a related technical discipline
 
 
 </li>
 </ul>
 It would also be great if you:
 
 
 <ul>
 <li>
 Are proficient with Kubernetes Workload Identity, managed identity architectures, and Azure Key Vault access security profiles
 </li>
 <li>
 Are familiar with Azure AI Foundry and API Management platforms, specifically controlling token consumption quotas, routing, and rate limits
 </li>
 <li>
 Are familiar with Azure Event Hubs or Kafka consumer groups, alongside immutable ADLS Gen2 storage structure management
 </li>
 <li>
 Hold an active Azure Administrator Associate (AZ-104), Azure DevOps Engineer Expert (AZ-400), or Certified Kubernetes Administrator (CKA) designations
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 
 The benefits you'll enjoy as part of this role include:
 
 
 
 <ul>
 <li>
 Competitive salary
 </li>
 <li>
 Comprehensive personal health insurance
 </li>
 <li>
 Visa Sponsorship for the successful individual
 </li>
 <li>
 Professional development and certification support
 </li>
 <li>
 Subscription reimbursement relating to your role
 </li>
 <li>
 Opportunity to work on cutting-edge AI projects
 </li>
 <li>
 Monthly Employee Incentive program
 </li>
 <li>
 Career advancement opportunities in a rapidly growing AI company
 
 
 </li>
 </ul>
 
 This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients.
 
 
 At DeepLight AI, we recognise that diversity drives innovation. We are committed to fostering an inclusive environment where individuals with different thinking styles can thrive and contribute their unique strengths to our specialised AI and data solutions.
 
 
 
 
 Our goal is to ensure our application and interview process is accessible, predictable, and fair for all candidates.
 
 
 
 
 If you require any specific adjustments to the application process, or if you require any reasonable adjustments should you be successful in being processed to the interview stage, please do let us know. This information will be kept strictly confidential and will not impact hiring decisions.
 
</div>

DevOps Engineer

Salt

<div>
 
 
 Job Title &ndash; Ai Engineer
 
 
 
 
 Salary &ndash; AED 25,000 - 28,500 Per Month
 
 
 
 
 Contract Length &ndash; 12 Months Contract
 
 
 
 
 Location &ndash; Dubai
 
 
 
 
 Start Date - ASAP
 
 
 
 
 
 
 My client is a global data, AI, digital transformation, and consulting company that helps organisations use data and technology to improve business performance, customer experience, and decision-making.
 
 
 
 
 
 
 Role Overview
 
 : The AI Engineer is the primary builder within the AWA AICoE. You will design, build, and test the LangGraph agent graphs, LLM extraction pipelines, prompt management workflows, and MCP tool integrations that power AWA's use cases &mdash; starting with Intelligent Document Processing (IDP) for cheque clearing and expanding across the bank's operations. You will work directly with the dual-layer orchestration stack (Orkes Conductor + LangGraph) and Azure AI Foundry to deliver production-grade agentic systems.
 
 
 
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Agent graph development: Design and implement LangGraph agent graphs for Supervisor-Worker orchestration, self-reflection, actor-critic, and HITL interrupt patterns using TypedDict state schemas and conditional edge routing.
 </li>
 <li>
 LLM extraction pipeline: Build and maintain LLM-based entity extraction pipelines using Azure AI Foundry, implementing structured output enforcement, per-field confidence scoring, and Pydantic output validation against Use Case Manifest schemas.
 </li>
 <li>
 Prompt engineering and governance: Author, version, and govern prompts through the AWA Prompt Management System (PMS) using semantic versioning; run prompt sensitivity and correctness testing using the AWA IDP Test Strategy.
 </li>
 <li>
 MCP tool integration: Develop and maintain the five core MCP tool servers (query_structured, retrieve_precedents, query_documents, checkpoint_state, observe) and extend them for new use case requirements.
 </li>
 <li>
 Model evaluation and testing: Execute Band 1 and Band 2 testing (Model Testing, Prompt Testing, Agent Testing, AI Security Testing, Adversarial Testing) per the AWA IDP Test Strategy; maintain the Golden Dataset and Regression Dataset.
 </li>
 <li>
 HITL framework: Implement HITL interrupt() calls at mandatory and dynamic review points in LangGraph graphs; configure Orkes Wait tasks; validate HITL state persistence and checkpoint-based resumption.
 </li>
 <li>
 AI guardrail integration: Integrate Azure AI Content Safety, PII detection and tokenisation, prompt injection shielding, and output sanitisation into agent pipelines at the AWA AI Gateway layer.
 </li>
 <li>
 Cross-field validation rules: Implement business validation rules (amount-words-vs-figures, date validity, MICR format, referential integrity) within LangGraph validation agent nodes.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Required Skills and Experience
 
 
 
 
 Technical &mdash; Essential
 
 
 <ul>
 <li>
 5+ years Python development &mdash; production-grade, not just scripting
 </li>
 <li>
 Hands-on experience with LangGraph, LangChain, or equivalent agentic AI frameworks
 </li>
 <li>
 Practical LLM prompt engineering: structured outputs, few-shot design, output schema enforcement
 </li>
 <li>
 Familiarity with Azure AI Foundry / OpenAI API or equivalent LLM API (Anthropic, Cohere)
 </li>
 <li>
 Understanding of agent design patterns: ReAct, self-reflection, tool use, HITL
 </li>
 <li>
 REST API development and consumption; JSON schema design
 </li>
 <li>
 Git version control; CI/CD pipeline awareness
 </li>
 </ul>
 
 
 
 
 
 Technical &mdash; Advantageous
 
 
 <ul>
 <li>
 Experience with Orkes Conductor, Apache Airflow, or Temporal for workflow orchestration
 </li>
 <li>
 Azure AI Document Intelligence or equivalent OCR platform experience
 </li>
 <li>
 Computer vision model integration (object detection APIs)
 </li>
 <li>
 Knowledge of RAG architectures: chunking, vector indexing, Azure AI Search
 </li>
 <li>
 Azure platform familiarity: AKS, ADLS Gen2, Azure ML, Azure Monitor
 </li>
 <li>
 Pydantic, FastAPI, TypeScript/Node.js
 </li>
 </ul>
 
 
 
 
 
 Domain and Soft Skills
 
 
 <ul>
 <li>
 Genuine intellectual curiosity about AI systems and their failure modes
 </li>
 <li>
 Ability to explain AI behaviour to non-technical Ops and business stakeholders
 </li>
 <li>
 Structured problem-solving and debugging approach for non-deterministic AI systems
 </li>
 <li>
 Banking domain interest &mdash; willingness to deeply understand the business processes being automated
 </li>
 </ul>
 
 
 
 
 Thanks,
 
 
 Ollie
 
 
 
 
</div>

Ai Engineer

Fuse Energy

<div>Fuse Energy is a forward-thinking renewable energy startup on a mission to deliver a terawatt of renewable energy - fast. We're combining first-principles thinking with cutting-edge technology to build a radically better energy system. We raised $210M from top-tier investors including Multicoin, Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, Box Group and strategic angels like Nico Rosberg, the Co-Founder of Solana and GPs behind Meta, Revolut, Spotify, Uber and more. 
 
We&rsquo;re creating a fully integrated energy company: from developing solar, wind and hydrogen projects to real-time power trading and distributed energy installations. By selling directly to consumers, we cut out the middleman, lower costs and pass on savings to customers. 
 
But we&rsquo;re not stopping there. We&rsquo;re also building the Energy Network: a decentralised platform of smart devices to electrify our homes, shifting usage to off-peak hours and helping balance the grid. This network strengthens grid stability - a critical foundation for scaling AI data centers and other energy-intensive industries. 
 
We're now looking to establish a cutting-edge AI team. As an Applied AI Engineer, this position is ideal for someone who possesses the technical expertise of a backend engineer but is specifically interested in applied AI and how it can be used to enhance the energy experience for our customers and our internal operations. You'll be working on a variety of exciting projects, including consumer-focused features like the Energy Co-Pilot and the Speedy Onboarding process (leveraging tools such as VLM/LLM). You will also collaborate across teams to build AI tools that enhance productivity and streamline processes within Fuse. 
 
Responsibilities<ul><li>
Design, develop and deploy AI-powered features that directly impact consumer experiences, including personalised energy recommendations and seamless onboarding via AI models (e.g. using energy bills for quick setup)</li><li>
Build and optimise internal AI tools that will make the whole company more productive with a focus on automation and enhancing workflows</li><li>
Collaborate with backend engineers and data scientists to integrate AI-driven features into our platforms</li><li>
Collaborate with the trading and operations teams to ensure AI models are aligned with real-time market conditions and energy pricing</li><li>
Improve AI models to optimise trading strategies by anticipating market shifts based on weather and demand forecasts</li><li>
Stay up to date with the latest advancements in applied AI and machine learning and apply them to solve real-world problems within the energy space</li>
Monitor the performance of AI tools and models, ensuring they are functioning efficiently and effectively 
<li>
</li></ul> 
 
Requirements 
<ul><li>
Minimum 3 years of engineering experience</li><li>
Proven experience as a Backend Engineer with a strong interest and practical experience in applied AI or machine learning</li><li>
Strong programming skills in Python (or similar languages) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.)</li><li>
Experience working with large-scale models (LLMs/VLMs) and deploying AI-driven solutions into production</li><li>
Solid understanding of cloud technologies, containerisation and building scalable AI applications</li><li>
Ability to integrate AI/ML models into real-world applications, focusing on usability and performance</li><li>
Strong problem-solving skills and a practical approach to implementing AI solutions in a fast-paced environment</li><li>
Experience working with large datasets, particularly in relation to demand and supply forecasting</li></ul>
Bonus<ul><li>
Experience or strong interest in energy markets and trading strategies</li><li>
Understanding of weather forecasting, energy demand patterns, and production modelling</li><li>
Exposure to Natural Language Processing (NLP) or other related fields</li></ul> 

Benefits 
<ul><li>
Competitive salary and an equity sign-on bonus</li><li>
Biannual bonus scheme</li><li>
Fully expensed tech to match your needs</li><li>
Paid annual leave</li><li>
Breakfast and dinner allowance for office based employees</li></ul></div>

Applied AI Engineer

<div>
 
 
 Job Title
 
 &ndash; AI Engineer
 
 
 
 Company
 
 &ndash; TCS (MEA)
 
 
 
 Location
 
 &ndash; Dubai
 
 
 
 Job type
 
 &ndash; Full time
 
 
 
 
 
 
 About Us:
 
 
 
 Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization that has been partnering with many of the world&rsquo;s largest businesses in their transformation journeys for over 50 years. TCS offers a consulting-led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions. This is delivered through its unique Location Independent Agile&trade; delivery model, recognized as a benchmark of excellence in software development.
 
 
 A part of the Tata group, India's largest multinational business group, TCS has over 616,171 of the world&rsquo;s best-trained consultants with 157 nationalities in 53 countries. For more information, visit www.tcs.com and follow TCS news at @TCS_News.
 
 
 
 
 
 
 
 Job Description:
 
 
 
 
 
 
 
 
 Key Accountabilities:
 
 
 <ul>
 <li>
 
 AI Solution Design &amp; Development:
 
 </li>
 <li>
 Design, build, and deploy AI-powered applications using Azure AI Services, including Azure OpenAI Service, Azure Machine Learning, Azure Cognitive Services (Speech, Vision, Language), Azure AI Search, Azure Functions, and Azure Databricks.
 </li>
 <li>
 
 LLM Integration &amp; Generative AI:
 
 </li>
 <li>
 Develop and integrate Large Language Model (LLM) solutions using Azure OpenAI endpoints (GPT-4.1, GPT-4o, GPT-4o-mini) routed through FAB's centralized AI Hub gateway for governance, observability, and capacity management. Build enterprise use cases such as knowledge search, document intelligence, content summarization, call analytics, sentiment analysis, and workflow automation.
 </li>
 <li>
 
 Python Engineering &amp; Backend Development:
 
 </li>
 <li>
 Write clean, modular, and production-grade Python code for AI/ML model development, API integrations, data processing pipelines, backend services, and automation workflows using frameworks such as LangChain, LlamaIndex, Semantic Kernel, LangGraph, and FastAPI.
 </li>
 <li>
 
 RAG &amp; Semantic Retrieval:
 
 </li>
 <li>
 Implement Retrieval-Augmented Generation (RAG) pipelines, embeddings, vector search, semantic retrieval architectures, prompt engineering, and evaluation techniques for enterprise knowledge mining and document intelligence.
 </li>
 <li>
 
 Agentic AI Development:
 
 </li>
 <li>
 Design and develop intelligent agentic AI systems capable of planning, reasoning, tool execution, and orchestration across enterprise systems using FAB's GERNAS OS platform and Agent Development Kit (ADK) in a secure and governed manner.
 </li>
 <li>
 
 Model Evaluation &amp; Responsible AI:
 
 </li>
 <li>
 Implement model evaluation metrics for accuracy, hallucination detection, bias, latency, and throughput in alignment with FAB's Responsible AI framework. Ensure AI solutions comply with FAB's security, data privacy, model governance, and regulatory requirements, including controls for accuracy, hallucination, bias, latency, and resilience.
 </li>
 <li>
 
 Deployment &amp; Production Operations:
 
 </li>
 <li>
 Deploy, monitor, and optimize AI solutions in production environments using CI/CD practices, containerization, observability, logging, performance monitoring, and cost optimization techniques.
 </li>
 <li>
 
 Collaboration &amp; Stakeholder Engagement:
 
 </li>
 <li>
 Collaborate with data scientists, platform engineers, DevOps/MLOps engineers, business analysts, and product teams to convert business requirements into scalable AI solutions.
 </li>
 <li>
 
 Innovation &amp; Continuous Learning:
 
 </li>
 <li>
 Continuously evaluate emerging Azure AI, Generative AI, and Agentic AI capabilities and recommend practical adoption opportunities aligned with FAB's AI strategy. Stay abreast of state-of-the-art developments in generative AI and agentic architectures.
 </li>
 </ul>
 
 
 
 
 
 TECHNICAL SKILLS: minimum 3-4 yrs of working experience mandatory
 
 
 
 
 Azure AI Services:
 
 Demonstrated working experience with Microsoft Azure AI Services including Azure OpenAI, Azure Machine Learning, Azure Cognitive Services, Azure AI Search, Azure Functions, and Azure Databricks.
 
 
 
 Python Programming:
 
 Strong proficiency in Python programming, including experience with REST APIs, SDKs, asynchronous processing, data manipulation, backend development, and AI application frameworks (LangChain, LlamaIndex, Semantic Kernel, LangGraph, FastAPI).
 
 
 
 LLM &amp; Generative AI:
 
 Deep understanding of machine learning, statistical modeling, NLP, generative AI principles, LLM application development, prompt engineering, RAG architecture, embeddings, vector databases, semantic search, and model evaluation techniques.
 
 
 
 ML Libraries &amp; Frameworks:
 
 Advanced proficiency in ML libraries such as PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and NLP libraries (spaCy, NLTK).
 
 
 
 Vector Databases:
 
 Experience with vector databases including FAISS, Azure AI Search, ChromaDB, and Pinecone.
 
 
 
 DevOps/MLOps:
 
 Hands-on experience with DevOps/MLOps practices and tools such as Git, Docker, Kubernetes, CI/CD pipelines, MLflow, Terraform, Azure Monitor, and Application Insights.
 
 
 
 Cloud Security &amp; Integration:
 
 Understanding of cloud security, identity and access management, data privacy, encryption, logging, monitoring, and secure API integration patterns.
 
 
 
 AI Ethics &amp; Governance:
 
 Awareness of ethical considerations and responsible AI practices, including fairness, accountability, transparency, bias detection, hallucination mitigation, and compliance in AI systems.
 
 
 
 
 
 
 KNOWLEDGE, SKILLS, &amp; EXPERIENCE
 
 
 
 
 
 Minimum Qualifications:
 
 
 
 
 Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, Statistics, or related field; Master's degree is preferred. Microsoft Certified: Azure AI Engineer Associate (AI-102) certification is highly preferred.
 
 
 
 
 Minimum Experience:
 
 
 
 
 3+ years of hands-on experience in designing, developing, and deploying AI/ML or Generative AI solutions in production environments.
 
 
 Mandatory hands-on experience with Microsoft Azure AI Services.
 
 
 Experience working with large-scale datasets and real-time enterprise data.
 
 
 Experience in financial services, banking, risk, compliance, customer service, or regulated enterprise environments is an advantage.
 
 
 
 
 Knowledge and Skills:
 
 
 
 
 
 Business Understanding:
 
 
 
 Strong understanding of business processes and the ability to identify opportunities for AI-driven optimization and automation across banking domains.
 
 
 
 Analytical &amp; Problem-Solving:
 
 
 
 Excellent analytical, problem-solving, debugging, and performance optimization skills with the ability to troubleshoot AI applications in production and translate complex business problems into AI solutions.
 
 
 
 Communication Skills:
 
 
 
 Strong verbal and written communication skills, capable of explaining complex AI concepts to both technical and non-technical stakeholders.
 
 
 
 Collaboration:
 
 
 
 Proven ability to work effectively in cross-functional teams, collaborating with data scientists, platform engineers, DevOps engineers, business analysts, and product teams.
 
 
 
 Responsible AI Mindset:
 
 
 
 Understanding of AI governance, model risk, data privacy, security, and compliance requirements in a regulated banking environment.
 
 
 
 Production Engineering:
 
 
 
 Ability to build, deploy, monitor, and optimize production-grade AI systems with attention to reliability, scalability, cost, and operational excellence.
 
 
 
 Adaptability &amp; Learning:
 
 
 
 Willingness to stay updated on the latest AI technologies, agentic frameworks, and cloud services, and the ability to apply them to solve evolving business challenges.
 
 
 Thank you for your interest in applying for this position with TCS. We will review your application and will get back to you if we are considering your interest in this opportunity.
 
 
 
 
 
 
 Application Deadline: 30- July -2026
 
 
 
 
 
 
 
 Privacy Note:
 
 
 
 https://www.tcs.com/connect-with-tcs/privacy-policy
 
</div>

AI Engineer

Kingston Stanley

<div>
 
 We&rsquo;re looking for a hands-on
 
 Azure Cloud Engineer
 
 to join a modern, Infrastructure-as-Code-driven environment built entirely on automation, scalability, and AI-first engineering practices.
 
 
 You&rsquo;ll work in a 100% cloud-native estate on Microsoft Microsoft Azure, owning and evolving core platform services across compute, networking, security, and DevOps.
 
 
 
 
 
 
 Tech Stack
 
 
 
 Terraform / Terragrunt | Azure DevOps | AKS &amp; Helm | PowerShell | Ansible | Sentinel | App Insights | Prometheus | Grafana | Loki
 
 
 
 
 
 
 What You&rsquo;ll Do
 
 
 <ul>
 <li>
 Build &amp; run Azure infrastructure at scale (VMs, AKS, networking, DR)
 </li>
 <li>
 Own Infrastructure-as-Code (modular Terraform/Terragrunt)
 </li>
 <li>
 Manage CI/CD pipelines (90+ Azure DevOps pipelines)
 </li>
 <li>
 Operate Kubernetes platforms &amp; observability stack
 </li>
 <li>
 Drive automation across identity, security, and operations
 </li>
 <li>
 Support multi-region DR and enterprise-grade resilience
 </li>
 </ul>
 
 
 
 
 
 Mindset
 
 
 <ul>
 <li>
 AI-first engineering (ChatGPT / Claude / Copilot)
 </li>
 <li>
 Automation-first, IaC-first approach
 </li>
 <li>
 Strong ownership in a senior, fast-moving team
 </li>
 </ul>
 
 
 
 
 
 Must Have
 
 
 
 
 Azure | Terraform | AKS | Azure DevOps | PowerShell | Networking | AD/Entra ID
 
 
 
 
 
 
 
 Salary: AED 22k standard benefits
 
 
</div>

IT Cloud Engineer Azure

Explore 490+ Ai platform engineer in Dubai this July. Find roles across top companies and apply now to boost your career.