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ai ml engineer remote

YO IT Consulting

<div>
 
 Job Title:
 
 AI/ML Engineer
 
 
 
 Job Type:
 
 Full-Time
 
 
 
 Job Summary
 
 
 
 Join our team as a AI/ML Engineer and play a pivotal role in driving mission-critical government initiatives while also contributing to proprietary AI infrastructure, you&rsquo;ll harness advanced large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration to deliver secure, scalable, and impactful machine learning systems. You&rsquo;ll work closely with cross-functional experts, leveraging frontier AI platforms and the latest in agentic AI tooling.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design, implement, and optimize AI/ML models particularly leveraging LLMs, RAG, and prompt engineering for production grade applications.
 </li>
 <li>
 Develop and orchestrate multi-agent systems using frameworks such as LangGraph and LangChain.
 </li>
 <li>
 Integrate and deploy solutions on secure cloud environments, including AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, and AWS Bedrock.
 </li>
 <li>
 Build robust data pipelines, manage ETL processes, and develop metadata catalogs and ontologies to ensure high-quality data for AI training and inference.
 </li>
 <li>
 Create and maintain REST APIs and SDK integrations to facilitate seamless data and model interactions.
 </li>
 <li>
 Collaborate with product, security, and engineering teams to ensure best-in-class delivery, following secure coding and DevOps best practices (CI/CD).
 </li>
 <li>
 Document technical decisions and communicate complex concepts clearly to both technical and non-technical stakeholders.
 
 
 </li>
 </ul>
 
 Required Skills And Qualifications
 
 
 
 <ul>
 <li>
 Proficiency in Python for AI/ML development, including experience with REST APIs and SDK integration.
 </li>
 <li>
 Hands-on experience with LLMs, RAG systems, and prompt engineering in production environments.
 </li>
 <li>
 Familiarity with multi-agent orchestration, tool use, and frameworks like LangGraph and LangChain.
 </li>
 <li>
 Deep understanding of cloud AI services (AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, AWS Bedrock).
 </li>
 <li>
 Background in building and maintaining data pipelines, ontologies, metadata catalogs, and ETL processes.
 </li>
 <li>
 Strong grasp of secure coding and modern DevOps practices (CI/CD pipelines).
 </li>
 <li>
 Exceptional written and verbal communication skills for effective collaboration and documentation.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with AI/agentic platforms, such as Anthropic for Gov, OpenAI Enterprise, Gemini Enterprise, or Grok Enterprise.
 </li>
 <li>
 Knowledge of metadata catalog platforms (MCP) and advanced API development techniques.
 </li>
 <li>
 Prior exposure to government or highly regulated cloud environments and compliance standards.
 </li>
 </ul>
</div>

AI/ML Engineer - Remote

Socium - Teams Done Differently

<div>
 
 
 General Description
 
 
 
 Join a fast-growing, technology-driven organization delivering advanced digital platforms and AI-enabled solutions at scale. We are looking for a
 
 Senior QA Engineer with strong automation expertise and exposure to AI/ML systems
 
 to help ensure high quality, reliability, and performance across modern enterprise applications.
 
 
 
 
 
 This role is ideal for a
 
 hands-on, senior-level QA engineer who can independently design and implement automation frameworks, validate complex system behaviors, and support quality assurance for AI-driven features and intelligent platforms.
 
 
 
 You will work closely with engineering, product, and platform teams to build scalable automation strategies and ensure high-confidence releases across APIs, applications, and AI-powered components.
 
 
 
 
 
 This is a
 
 freelance / contract, hybrid role with an urgent hiring timeline
 
 , supporting engineering teams building high-impact digital and AI-enabled solutions.
 
 
 
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Design, develop, and maintain scalable
 
 test automation frameworks for UI, API, and end-to-end testing
 
 .
 </li>
 <li>
 Implement automated regression suites integrated with
 
 CI/CD pipelines
 
 to ensure fast and reliable releases.
 </li>
 <li>
 Validate complex workflows, distributed systems, and
 
 microservices-based architectures
 
 .
 </li>
 <li>
 Collaborate with developers and product teams to
 
 define test strategies, acceptance criteria, and quality standards
 
 early in the development lifecycle.
 </li>
 <li>
 Identify test coverage gaps and improve automation coverage across critical system components.
 </li>
 <li>
 Support testing of
 
 AI/ML-powered features
 
 , including validation of model outputs, response accuracy, and system behavior.
 </li>
 <li>
 Develop strategies for
 
 test data management, environment stability, and automated test execution
 
 .
 </li>
 <li>
 Perform root cause analysis of defects and work closely with engineering teams to improve system reliability.
 </li>
 <li>
 Contribute to
 
 quality metrics, reporting, and release readiness
 
 .
 </li>
 <li>
 Drive
 
 automation-first testing practices
 
 and mentor team members where needed.
 </li>
 </ul>
 
 
 
 
 
 Required Skills &amp; Experience
 
 
 
 At least
 
 7&ndash;10+ years of experience in software quality assurance
 
 , with strong focus on
 
 test automation
 
 .
 
 
 Proven experience designing and implementing
 
 automation frameworks from scratch
 
 .
 
 
 Strong hands-on experience with automation tools such as:
 
 <ul>
 <li>
 Selenium / Cypress / Playwright
 </li>
 <li>
 REST API automation tools (Postman, RestAssured, or similar)
 </li>
 </ul>
 
 Strong programming or scripting experience in
 
 Python, Java, or JavaScript
 
 .
 
 
 Experience testing
 
 API-driven architectures and microservices environments
 
 .
 
 
 Solid understanding of
 
 CI/CD pipelines and automated testing integration
 
 .
 
 
 Experience validating
 
 complex test scenarios and enterprise-scale systems
 
 .
 
 
 Exposure to
 
 AI/ML systems or AI-enabled applications
 
 , including testing model outputs, prompts, or intelligent system behaviors.
 
 
 Strong debugging, analytical thinking, and problem-solving skills.
 
 
 Ability to work
 
 independently in a contract environment
 
 with minimal supervision.
 
 
 Excellent communication and collaboration skills across technical teams.
 
 
 
 
 
 
 Preferred / Nice to Have
 
 
 <ul>
 <li>
 Experience testing
 
 AI-powered applications, LLM-based systems, or data-driven platforms
 
 .
 </li>
 <li>
 Familiarity with
 
 performance testing tools (JMeter, k6, or similar)
 
 .
 </li>
 <li>
 Experience working in
 
 cloud environments (AWS, Azure, or GCP)
 
 .
 </li>
 <li>
 Background in
 
 DevOps practices and containerized environments (Docker/Kubernetes)
 
 .
 </li>
 <li>
 Experience working in
 
 enterprise or high-scale distributed systems
 
 .
 </li>
 </ul>
</div>

Senior QA Engineer (Automation + AI / ML)

Confidential

<div>
 
 
 Job Summary
 
 
 
 We are seeking a
 
 Senior / Lead Data Engineer
 
 to design, build, and scale
 
 AI-powered, cloud-native data platforms
 
 that support enterprise analytics, machine learning, and business-critical decision-making.
 
 
 The ideal candidate will bring deep expertise in
 
 data engineering, cloud data warehouses, cost optimization, and AI-assisted development
 
 , with a proven track record of delivering
 
 high-impact, revenue-generating data platforms
 
 and
 
 intelligent automation solutions
 
 at scale.
 
 
 
 Key Responsibilities
 
 
 
 
 Data Platform &amp; Pipeline Engineering
 
 
 <ul>
 <li>
 Design, develop, and maintain
 
 scalable ETL/ELT pipelines
 
 using Python, SQL, dbt, and modern orchestration tools.
 </li>
 <li>
 Build and optimize
 
 cloud-based data warehouses
 
 (BigQuery or equivalent) supporting high-volume analytical workloads.
 </li>
 <li>
 Implement
 
 robust data modeling
 
 (dimensional and analytical models) to enable self-service analytics and AI use cases.
 </li>
 <li>
 Ensure
 
 data quality, integrity, and freshness
 
 through automated validation frameworks and monitoring.
 </li>
 </ul>
 
 
 AI-Driven Automation &amp; Innovation
 
 
 <ul>
 <li>
 Design and deploy
 
 AI-powered internal tools and agents
 
 to automate data engineering workflows, incident diagnosis, and root cause analysis.
 </li>
 <li>
 Integrate AI agents with collaboration tools (e.g., Slack) to improve operational efficiency and reduce incident resolution time.
 </li>
 <li>
 Apply AI-assisted development techniques to accelerate delivery, improve code quality, and reduce manual effort across teams.
 </li>
 </ul>
 
 
 Performance &amp; Cost Optimization
 
 
 <ul>
 <li>
 Lead
 
 cloud cost optimization initiatives
 
 , including query optimization, architectural redesign, and usage monitoring.
 </li>
 <li>
 Drive measurable reductions in data platform operational costs while maintaining performance and reliability.
 </li>
 </ul>
 
 
 Cloud &amp; DevOps
 
 
 <ul>
 <li>
 Build and deploy data services using
 
 containerized and CI/CD-driven workflows
 
 .
 </li>
 <li>
 Collaborate with DevOps and platform teams to ensure scalable, secure, and reliable cloud infrastructure.
 </li>
 <li>
 Support multi-cloud or hybrid environments as required (GCP, Azure).
 </li>
 </ul>
 
 
 Leadership &amp; Collaboration
 
 
 <ul>
 <li>
 Provide
 
 technical leadership and mentorship
 
 to data engineers.
 </li>
 <li>
 Establish
 
 SOPs, best practices, and engineering standards
 
 across the data function.
 </li>
 <li>
 Partner closely with product, analytics, and business stakeholders to translate requirements into scalable data solutions.
 </li>
 </ul>
 
 
 Required Skills &amp; Qualifications
 
 
 
 
 Technical Skills
 
 
 <ul>
 <li>
 
 Programming:
 
 Python (expert), SQL (expert), Shell scripting (advanced).
 </li>
 <li>
 
 Data Engineering:
 
 dbt, ETL/ELT pipelines, data warehousing, data modeling, data quality frameworks.
 </li>
 <li>
 
 Cloud Platforms:
 
 Google Cloud Platform (BigQuery, Cloud Run) &ndash; strong experience; Azure familiarity is a plus.
 </li>
 <li>
 
 Orchestration &amp; Automation:
 
 Airflow or equivalent.
 </li>
 <li>
 
 DevOps &amp; Tooling:
 
 Git, Docker, CI/CD pipelines.
 </li>
 <li>
 
 AI &amp; ML Exposure:
 
 AI agent frameworks, ML pipelines, model lifecycle management, observability tools.
 </li>
 </ul>
 
 
 Data Management
 
 
 <ul>
 <li>
 Cost optimization and performance tuning.
 </li>
 <li>
 Data governance, cataloging, and best-practice documentation.
 </li>
 </ul>
 
 
 Education
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Computer Science or related field.
 </li>
 <li>
 Master&rsquo;s or Postgraduate qualification in Data Science is preferred.
 </li>
 </ul>
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Experience delivering
 
 AI/ML pipelines
 
 in production environments.
 </li>
 <li>
 Exposure to
 
 government, public sector, or regulated enterprise data platforms
 
 .
 </li>
 <li>
 Experience building
 
 observability and monitoring tools
 
 for data platforms.
 </li>
 <li>
 Prior work on
 
 Arabic-language or regional datasets
 
 .
 </li>
 </ul>
 
 
</div>

Data Engineer

GenBio AI

<div>
 GenBio AI develops multiscale foundation models to decode and simulate human biology. Our team is accelerating towards an ambitious future where scientists can unlock humanity's biggest challenges in drug discovery, healthcare, and fundamental research with AIDO (AI-Driven Digital Organism): a unified framework for predicting, simulating, and programming biology across all scales. The foundation of this vision begins today as we engineer the virtual cell to model and simulate the fundamental unit of life.
 
 
 This vision has brought together a talent-dense group of product-minded researchers and engineers dedicated to bringing it to reality. Our team prides itself on our strong engineering culture and highly interdisciplinary and collaborative approach. We are based in Palo Alto, with satellite offices in Paris and Abu Dhabi.
 
 
 As our data ingestion needs grow, we are looking for a Bioinformatics Data Engineer to act as the crucial bridge between raw biological data and our scalable infrastructure. Reporting to the Data Engineering Lead, you will leverage your deep biological domain expertise to build the initial scripts and processing logic for complex datasets, ensuring they are primed for large-scale foundation model training.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Source &amp; Acquire Biological Data - Identify, evaluate, and obtain high-quality bioinformatics datasets from public and partner sources (e.g., NCBI, PubChem, ENCODE,UniProt etc.) to support research and model development initiatives
 </li>
 <li>
 Deeply Understand Complex Datasets - Develop a comprehensive understanding of biological datasets, including data structures, schemas, metadata standards, entity relationships, and underlying biological context to ensure accurate interpretation and usage
 </li>
 <li>
 Design &amp; Implement Data Processing Pipelines - Develop robust preprocessing scripts and scalable data transformation workflows using Python, R, and relevant Tools. Leverage AI-assisted tools where appropriate to process, clean, normalize, and integrate complex biological data for foundation model training
 </li>
 <li>
 Structure &amp; Standardize Biological Data - Organize heterogeneous datasets into well-defined, interoperable formats aligned with internal infrastructure requirements and downstream AI training pipelines
 </li>
 <li>
 Bioinformatics Data Analysis - Perform exploratory and statistical analysis of genomic, transcriptomic, proteomic, and other multi-omics datasets to assess data quality, uncover biological patterns, and generate insights that inform model development. Apply appropriate computational and statistical methods to validate assumptions and support downstream AI training and evaluation
 </li>
 <li>
 Build Data Products - Create production-ready data assets, including standardized datasets, curated releases, dashboards, analytical reports, and technical documentation to enable efficient research and model evaluation
 </li>
 <li>
 Ensure Data Quality &amp; FAIR Compliance - Curate, annotate, validate, and standardize public and partner datasets in alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles, ensuring long-term usability and reproducibility
 </li>
 <li>
 Collaborate Cross-Functionally - Partner closely with research scientists and ML engineers to translate biological research needs into scalable data engineering solutions that support AI model training and evaluation
 </li>
 <li>
 Knowledge Sharing &amp; Documentation - Contribute domain expertise by documenting data methodologies, maintaining clear technical documentation, and sharing biological data insights across teams
 
 
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Educational Background: Bachelor&rsquo;s or Master&rsquo;s degree in Bioinformatics, Computational Biology, Computer Science, or a related field with a heavy focus on the life sciences
 </li>
 <li>
 Biological Data: Deep, hands-on familiarity with multiple biomedical data modalities (e.g., genomics, transcriptomics, spatial omics, protein structure, biomedical imaging, clinical/phenotypic data, etc.)
 </li>
 <li>
 Biological Tools: Familiar with Bioconda,Biopython,Bioconductor, samtools,bamtools,bcftools,gffutils etc
 </li>
 <li>
 Scripting &amp; Tooling: Strong programming skills in Python (Pandas, NumPy) and proficiency with standard bioinformatics workflow managers and tools (e.g., Ray, Kubeflow)
 </li>
 <li>
 Engineering Handoff: Experience writing clean, modular code that can be easily picked up by core data engineers for optimization in cloud environments (AWS/GCP/HF) and containerized setups (Docker)
 </li>
 <li>
 AI/ML Awareness: A solid understanding of machine learning workflows and how biological data must be formatted and batched for deep learning frameworks (e.g., PyTorch)
 
 
 
 
 </li>
 </ul>
 Join us as we embark on this journey to redefine the future of biology and medicine.
 
 
 We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. GenBio AI participates in the U.S. Department of Homeland Security&rsquo;s E-Verify program to confirm the employment eligibility of all newly hired employees. For more information on E-Verify, please visit www.e-verify.gov.
 
 
 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
</div>

Bioinformatics Data Engineer

Vtricks Technologies 

Hiring: Business Analyst &ndash; Corporate Banking (AI/ML Focus)Location: Abu Dhabi, UAE Company: BigTapp Analytics (Client: Tier-1 Bank) Joining: Immediate Joiners Only Work Authorization: Valid UAE Work Visa MandatoryRole OverviewWe are hiring an experienced Business Analyst with strong Corporate Banking domain expertise to work with a leading bank in Abu Dhabi. This role bridges business stakeholders and AI/ML delivery teams, translating banking requirements into actionable data science use cases across areas like customer experience, risk, revenue, and operations.Key Responsibilities<ul><li>Engage with senior banking stakeholders to identify AI/ML opportunities</li><li>Translate business needs into detailed ML use cases &amp; functional specs</li><li>Define KPIs, data requirements, and business constraints</li><li>Act as liaison between business, data engineers, and data scientists</li><li>Support UAT, validation, and model performance tracking</li><li>Present insights and recommendations to leadership</li></ul>Domain Expertise (Must Have)<ul><li>7&ndash;10+ years in Corporate Banking</li><li>Strong exposure to at least 3 areas:</li><li>Corporate Credit / Risk</li><li>Trade Finance</li><li>Cash Management</li><li>Treasury / Lending</li><li>Relationship Management</li><li>Understanding of banking KPIs (NIM, RWA, CLV)</li></ul>Required Skills<ul><li>Experience in Business Analysis (BRD, FSD, use cases)</li><li>Knowledge of AI/ML concepts (basic understanding required)</li><li>Strong stakeholder management &amp; communication skills</li><li>Ability to work in data-driven environments</li></ul>Preferred<ul><li>Experience with UAE / GCC banking domain</li><li>Exposure to AI/ML or digital transformation projects</li><li>Certifications: CBAP / Data Analytics / AI</li></ul>Important Requirements<ul><li>Immediate joiners only</li><li>Candidate must have valid UAE work visa</li></ul>Pay: AED9,500.00 - AED12,500.00 per monthApplication Question(s):<ul><li>What is your notice period in days?</li></ul>Experience:<ul><li>Business Analyst with strong Corporate Banking domain : 7 years (Required)</li></ul>Location:<ul><li>Abu Dhabi (Required)</li></ul>Work Location: In person

Business Analyst – Corporate Banking (AI/ML Focus)

Khazna Data Centers

<div>
 
 
 Khazna was founded in 2012 and has grown rapidly into becoming the leading and trusted wholesale Data Center provider in the Middle East and North Africa region. Through our Data Centers, we provide industry benchmark levels of power supply and cooling services to better serve the growing need for data center operations in the UAE and wider region.
 
 
 
 
 
 
 We are seeking a
 
 Data Engineer
 
 to join our
 
 Command &amp; Control Center &ndash; Data Analytics &amp; Reporting
 
 team, reporting to the
 
 Manager, Data &amp; BI
 
 . This centralized role is responsible for designing, developing, and maintaining scalable data pipelines that support analytics, reporting, and AI/ML initiatives for data center operations. The Data Engineer will ensure data integrity, optimize data architecture, and manage ETL (Extract, Transform, Load) processes across cloud and on-premises platforms to enable effective, data-driven decision-making.
 
 
 
 
 
 
 Key Accountabilities:
 
 
 
 
 
 <ul>
 <li>
 Support the development of scalable data warehouses, lakes and resilient data pipelines to support high-velocity data processing in a wholesale data center environment
 </li>
 <li>
 Design and implement Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes to clean, normalize and transform data from diverse sources, including Internet of Things (IoT) sensors, Building Management System (BMS) within data center operations
 </li>
 <li>
 Automate data ingestion, transformation and storage workflows for real-time analytics, ensuring low-latency insights for operational decision-making across data center environments
 </li>
 <li>
 Set up streaming data services to enable continuous migration of critical data center infrastructure metrics into analytics platforms for real-time monitoring
 </li>
 <li>
 Work with Integration teams, Data Warehouse engineers and Analytics teams to build highly optimized, resilient and scalable data center data processing architectures
 </li>
 <li>
 Define data sourcing strategies, tagging mechanisms and aggregation methods to ensure seamless availability of structured and unstructured data for data center analytics
 </li>
 <li>
 Collaborate with Data Scientists and Business Intelligence (BI) teams to enable AI/ML model development, predictive analytics and operational intelligence for data center operations
 </li>
 <li>
 Develop calculation methodologies for statistical computation of KPIs related to Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), uptime and asset utilization in data centers
 </li>
 <li>
 Identify critical data points and sources that drive KPI computations and aggregate site-level metrics for centralized data center performance reporting
 </li>
 <li>
 Build reporting source files for dashboards, enabling executive leadership to monitor data center performance trends across regional and site-level operations
 </li>
 <li>
 Generate historical trend analyses, benchmarking individual data center site performance against portfolio-wide averages to drive efficiency improvements
 </li>
 <li>
 Conduct Exploratory Data Analysis (EDA) to identify patterns, anomalies and correlations that impact energy efficiency, workload distribution and infrastructure health in data centers
 </li>
 <li>
 Gather required historical and real-time data from internal platforms and external sources, ensuring comprehensive datasets for advanced analytics in data center environments
 </li>
 <li>
 Develop simulations and scenario models to analyse potential business impacts, optimize data center capacity planning and mitigate operational risks
 </li>
 <li>
 Update models and forecasting frameworks based on market trends, data center infrastructure expansion plans and internal priority shifts
 </li>
 <li>
 Research and implement cutting-edge data engineering techniques, contributing to industry-leading best practices in large-scale data center data infrastructure management
 </li>
 <li>
 Deliver data archiving, backup and disaster recovery strategies, ensuring high availability and business continuity for critical data center data assets
 </li>
 <li>
 Ensure seamless integration of data platforms with enterprise applications, enabling end-to-end visibility and automated decision-making across the data center ecosystem
 </li>
 </ul>
 
 
 
 
 
 Minimum Qualifications:
 
 
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or master&rsquo;s degree in computer science (or) related to Data Engineering
 </li>
 <li>
 Certifications in cloud data platforms preferred
 </li>
 <li>
 4+ years of experience in data engineering, ETL development or database management
 </li>
 <li>
 Strong proficiency in SQL, Python or Scala
 </li>
 <li>
 Experience with cloud data platforms (AWS, Azure, GCP)
 </li>
 <li>
 Hands-on experience with data pipeline tools like Apache Airflow, Kafka or Spark
 </li>
 <li>
 Understanding of data modeling, warehousing and governance principles
 </li>
 </ul>
 
 
 
 
 
 Additional skills:
 
 
 
 
 
 <ul>
 <li>
 Ability to troubleshoot complex data challenges, optimize pipeline performance and drive continuous improvements in large-scale infrastructure
 </li>
 <li>
 Strong ability to work across Data Science, Engineering, IT and Business Intelligence teams to align data solutions with business needs
 </li>
 <li>
 Expertise in translating technical data models and engineering concepts into clear insights for non-technical stakeholders
 </li>
 </ul>
 
 
 
 
 
 Training/Certifications Preferred:
 
 
 
 
 
 <ul>
 <li>
 AWS Certified Data Analytics &ndash; Specialty
 </li>
 <li>
 Google Professional Data Engineer
 </li>
 <li>
 Microsoft Certified: Azure Data Engineer Associate
 </li>
 <li>
 Apache Spark Developer Certification
 </li>
 </ul>
 
 
</div>

Dizzaract FZ LLC

About DizzaractDizzaract is a UAE-based game development studio founded in 2022, headquartered at Yas Creative Hub, Abu Dhabi. We develop cutting-edge AI-powered games and systems, including our innovation R&amp;D laboratory FAR labs, the upcoming hero shooter Farcana, and the AI gaming identity platform GAMED. Our research and development team boasts over 100 peer-reviewed papers and more than 20 patents in AI-driven gameplay, digital ownership, and competitive design. With a diverse team of more than 80 professionals from over 20 countries, we are committed to innovation, excellence, and building a culture that drives performance and results.Role SummaryWe are seeking a strategic Business Developer with deep expertise in AI models, inference, and MLOps commercialization. You will be responsible for identifying, negotiating, and closing B2B partnerships to monetize our proprietary AI models and inference capabilities. This role sits at the intersection of technology and business &mdash; you will translate complex AI technical value (latency, throughput, model accuracy, cost per inference) into revenue-generating opportunities.Key Responsibilities1. AI Business Development &amp; Partnerships<ul><li>Identify and secure partnerships with enterprises, developers, and platforms needing AI inference solutions (LLMs, computer vision, generative AI for gaming).</li><li>Build a go-to-market strategy for FAR Labs&rsquo; proprietary AI models and inference infrastructure.</li><li>Develop licensing models: API calls, subscription tiers, on-prem inference, or revenue-share agreements.</li><li>Lead the full sales cycle: lead generation, technical scoping, proposal writing, contract negotiation, and closing.</li></ul>2. Monetization of Inference &amp; Model APIs<ul><li>Collaborate with engineering to define pricing strategies based on inference cost (compute, tokens, latency, batch size).</li><li>Package our R&amp;D output (20+ patents, 100+ papers) into sellable AI products: game AI agents, player behavior models, identity verification AI (GAMED).</li><li>Create technical marketing materials: case studies, inference benchmarks, model cards, and demos.</li></ul>3. Market &amp; Competitive Intelligence<ul><li>Track the AI inference landscape: cloud providers (AWS Bedrock, Azure AI, Replicate), open-source models, and emerging competitors.</li><li>Identify gaps where Dizzaract&rsquo;s models outperform in latency, cost, or domain-specific tasks (competitive gaming, real-time AI).</li><li>Provide feedback to R&amp;D on market needs (model size, quantization, edge inference requirements).</li></ul>4. Customer &amp; Technical Engagement<ul><li>Serve as the bridge between external clients and our ML engineering team &mdash; translate business needs into technical requirements (batch vs real-time inference, GPU/CPU tradeoffs, SLAs).</li><li>Support clients with POCs, integration of our inference APIs, and performance validation.</li><li>Collect and prioritize feature requests for our inference stack (scaling, observability, versioning).</li></ul>5. Strategic Growth &amp; OKRs<ul><li>Define and track BD KPIs: number of active partnerships, monthly recurring inference revenue (MRR), customer acquisition cost (CAC), and gross margin per inference.</li><li>Implement a quarterly OKR framework for the BD team aligned with company revenue goals.</li><li>Report directly to COO and the Director of BD on pipeline, forecasts, and strategic deals.</li></ul>Required Qualifications &amp; Skills<ul><li>3+ years in business development, partnerships, or technical sales focused on AI/ML models or inference platforms.</li><li>Strong understanding of inference concepts: model serving, batching, latency vs throughput, token-based pricing, fine-tuning, and quantization.</li><li>Ability to read basic API docs and discuss model performance with engineers (no need to code, but technical fluency is a must).</li><li>Proven track record of closing B2B SaaS or API-based deals, preferably with gaming, media, or security clients.</li><li>Familiarity with AI deployment tools (Hugging Face, TensorRT, vLLM, or cloud inference endpoints) is a strong plus.</li><li>Excellent negotiation, communication, and presentation skills for technical and non-technical audiences.</li><li>Self-starter mindset, comfortable with remote work and fast-paced R&amp;D environments.</li></ul>Final NoteThis role is for a business-savvy AI expert who wants to turn cutting-edge research into real revenue. You will have direct access to 20+ patents, a world-class R&amp;D team, and an emerging AI ecosystem in Abu Dhabi. If you understand inference pricing models, can sell APIs to developers and enterprises, and want to shape the commercial future of AI in gaming &mdash; this is your opportunity.Work Location: In person

Business Developer Executive

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AI/ML Engineer - Remote

Senior QA Engineer (Automation + AI / ML)

Data Engineer

Bioinformatics Data Engineer

Business Analyst – Corporate Banking (AI/ML Focus)

Data Engineer

Business Developer Executive