machine learning infrastructure engineer

Seeka Technology

<div>Company Description 
We believe strong early career roles should do more than give someone a job title. They should help people grow into the kind of professionals they want to become. At SEEKA Technologies, we give junior team members meaningful work that builds real capability, sharpens problem-solving, and develops practical experience in fast-moving business and technology environments. Our goal is to help emerging talent strengthen their skills, expand their potential, and prepare for a future shaped by constant innovation across business and IT.
SEEKA Technologies (Not Seeka Limited) is a project under its parent organisation, called Fresh Futures Australia, which is an education consultant based in both Australia and Malaysia. We are developing and creating a platform that utilises A.I. to help match students and job seekers to the right opportunities relevant to them from Kindergarten up to the University, along with vocational training centres and language schools, and of course to businesses and companies who need the right candidates. Our mission is to make it easier for anyone to find, filter and apply to educational institutions and companies in a more seamless manner.
We are currently looking to hire a junior Backend AI &amp; Data Pipeline Engineer who wants to build real-world experience in backend systems, data processing, scraping, retrieval, and cloud-based infrastructure. This role is ideal for someone who already has hands-on technical experience and wants to grow further by working on meaningful engineering challenges that support Yuzee&rsquo;s intelligent matching platform. You will contribute to the systems that process data, power search and matching, and improve the efficiency, reliability, and scalability of our platform.
Below are the important details you will need to take note of:<ul><li>
English is the primary language used in the role</li><li>
This is a full-time remote/work-from-home position</li><li>
We welcome both local and international candidates</li><li>
Candidates should have a degree or proven practical experience relevant to the role</li></ul> 

Job Description 
About the role
We are looking for a Backend AI &amp; Data Pipeline Engineer to own the end-to-end data processing infrastructure that powers Yuzee's intelligent course and job matching platform. You will design and maintain scalable, event-driven pipelines that process tens of thousands of daily records, generate semantic embeddings, and feed a growing knowledge graph used for personalised career pathway recommendations.
What you'll do<ul><li>
Design and maintain three distinct processing pipelines &mdash; scheduled job ingestion, event-driven course processing, and a periodic knowledge graph builder &mdash; each with independent trigger logic and cost controls</li><li>
Generate and manage semantic embeddings via Amazon Bedrock (Titan v2), index them in MongoDB Atlas Vector Search, and calibrate similarity thresholds to ensure match accuracy</li><li>
Build and maintain a knowledge graph linking jobs, courses, skills, and industries using FP-Growth association rules and archetype-to-SOC code mapping</li><li>
Build and improve a two-stage discovery and matching API on AWS Lambda &mdash; vector retrieval first, then deep eligibility scoring with LLM re-ranking</li><li>
Right-size Fargate Spot instances and design resumable processing loops that tolerate interruption, keeping infrastructure costs under control as data volume scales</li><li>
Maintain and improve daily job scrapers across multiple sources and build institution data scrapers with robust HTML cleaning pipelines</li></ul>
What we're looking for<ul><li>
1+ years of backend engineering experience focused on data pipelines, ML infrastructure, or search systems</li><li>
Hands-on experience with AWS serverless and container services &mdash; Lambda, ECS Fargate, EventBridge, and Step Functions</li><li>
Strong Python skills &mdash; Pandas, async processing, bulk database operations, and text cleaning</li><li>
Familiarity with vector databases and semantic similarity search; MongoDB Atlas Vector Search experience is a strong plus</li><li>
Cost-conscious infrastructure mindset &mdash; you think in per-record compute costs, free tiers, Spot resilience, and right-sizing</li><li>
Ability to document and communicate complex architecture clearly to both technical and non-technical stakeholders</li></ul>
Nice to have<ul><li>
Experience with knowledge graphs or association rule mining (FP-Growth, Apriori)</li><li>
Experience using LLMs for re-ranking or eligibility assessment on top of vector retrieval results</li><li>
Background in edtech, jobtech, or recommendation/matching systems</li></ul> 

Qualifications 
Degree or existing proven experience 
Additional Information 
Use this instead:
Benefits<ul><li>
Fully remote / work-from-home role</li><li>
Flexible working hours within the team&rsquo;s expected schedule and business needs</li><li>
Opportunity to work on real backend, data, and AI infrastructure projects</li><li>
Exposure to practical engineering challenges in scraping, pipelines, retrieval, and cloud systems</li><li>
Ongoing growth and development within a fast-moving technology environment</li><li>
Opportunity to build long-term value and grow with the company based on performance, including progression and increased responsibility over time</li></ul>
A slightly more polished version:
Benefits<ul><li>
Fully remote / work-from-home position</li><li>
Some flexibility in working hours, depending on team requirements and deliverables</li><li>
Hands-on experience working on meaningful backend, data pipeline, and AI-related systems</li><li>
Opportunity to contribute to a growing platform with real product and engineering challenges</li><li>
Professional growth in a practical, fast-paced environment</li><li>
Strong potential for long-term progression based on performance, regardless of location</li></ul>
If you want it to sound more attractive for hiring, the strongest version would be to add things like:<ul><li>
ownership</li><li>
real product impact</li><li>
career growth</li><li>
direct exposure to architecture and scaling decisions</li></ul></div>

Backend AI & Data Pipeline Engineer

Enigma Search

<div>
 
 
 Role Summary
 
 
 
 We are seeking a Machine Learning Engineer who has experience working with small
 
 
 molecule drug discovery datasets and can hit the ground running to deliver high-impact,
 
 
 production-grade solutions to advance our programs. The ideal candidate will build and
 
 
 scale data and ML infrastructure across early research, lead optimization, and development
 
 
 phases of drug discovery pipeline. You will enable data-driven science while ensuring
 
 
 robust engineering practices and FAIR data principles.
 
 
 
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Support management of biobank scale datasets in Polaris, Maze&rsquo;s internal platform
 </li>
 </ul>
 
 supporting Compass, by building scalable data ingestion, cleaning, processing, and
 
 
 validation pipelines.
 
 <ul>
 <li>
 Work with scientific compute teams to design and deploy machine learning models to
 </li>
 </ul>
 
 support workflows in research and small molecule drug discovery (compound property
 
 
 prediction, assay data prediction, data analysis).
 
 <ul>
 <li>
 Lead the evaluation and integration of Large Language Models (LLMs) to automate data
 </li>
 </ul>
 
 ingestion workflows, enhance intelligent querying, and support user-facing variant
 
 
 association and scientific visualization platforms.
 
 <ul>
 <li>
 Design and operate scalable ML and data platforms leveraging Terraform (IaC) and Git-
 </li>
 </ul>
 
 based CI/CD pipelines, incorporating workflow orchestration, automated model
 
 
 lifecycle management, and production-grade monitoring and reliability.
 
 <ul>
 <li>
 Collaborate with development organization to evaluate and deploy ML tools that
 </li>
 </ul>
 
 support workflows across Regulatory, Clinical Operations, and Medical Affairs.
 
 <ul>
 <li>
 Collaborate cross-functionally translate scientific requirements into production-grade
 </li>
 </ul>
 
 Systems.
 
 
 
 
 
 
 Required Qualifications
 
 
 <ul>
 <li>
 Master&rsquo;s degree in Computer Science, Machine Learning, Bioinformatics, Data
 </li>
 </ul>
 
 Engineering, or a related field.
 
 <ul>
 <li>
 3+ years of industry experience building production-grade data and ML pipelines,
 </li>
 </ul>
 
 preferably in life sciences supporting drug discovery.
 
 <ul>
 <li>
 Hands-on experience deploying AI/ML models in drug discovery applications (e.g.,
 </li>
 </ul>
 
 computational biology/chemistry workflows).
 
 <ul>
 <li>
 Experience with FAIR data principles and strong programming skills in Python and SQL
 </li>
 </ul>
 
 (R is a plus).
 
 <ul>
 <li>
 Proven experience in deploying and maintaining ML systems, including CI/CD,
 </li>
 </ul>
 
 workflow orchestration, and monitoring.
 
 <ul>
 <li>
 Experience with workflow orchestration tools (e.g., Airflow, Prefect).
 </li>
 <li>
 Experience with containerization and cloud infrastructure (Docker, Kubernetes, AWS or
 </li>
 </ul>
 
 similar).
 
 
 
</div>

Machine Learning Engineer - Drug Discovery

DeepSource Technologies

<div>We are looking for a highly capable Senior AI Engineer / MLOps Engineer to join our team and lead the design, development, deployment, and optimization of scalable, production-grade AI and machine learning solutions. The ideal candidate will have strong hands-on experience across AI engineering, machine learning, MLOps, cloud-native architecture, and data engineering, with the ability to transform experimentation into reliable, business-ready systems.
This role requires deep expertise in LLMs, RAG, agentic AI workflows, CI/CD automation, production ML lifecycle management, and modern data platforms. The selected candidate will be expected to lead end-to-end AI initiatives, work across multiple projects, collaborate with technical and business stakeholders, and ensure operational excellence across AI platforms.
Key Responsibilities<ul><li>Design, develop, deploy, and maintain production-grade AI and machine learning systems end to end.</li><li>Build and optimize LLM-powered applications, including RAG pipelines, prompt workflows, agent-based systems, and multimodal AI use cases.</li><li>Develop intelligent workflows using tool-calling, orchestration frameworks, and contextual reasoning patterns.</li><li>Fine-tune, evaluate, and operationalize machine learning and foundation models for enterprise use cases.</li><li>Build and manage MLOps pipelines covering training, evaluation, model registration, deployment, monitoring, and retraining.</li><li>Implement CI/CD pipelines for ML and AI workflows to support automated testing, release management, and controlled deployments.</li><li>Establish model monitoring frameworks for drift detection, feature attribution, inference quality, and performance tracking.</li><li>Ensure reproducibility, reliability, and version control across AI/ML environments.</li><li>Architect scalable AI/ML platforms using modern compute, storage, orchestration, monitoring, and search services.</li><li>Build repeatable environments using Infrastructure as Code.</li><li>Support secure, high-availability, and cost-efficient deployment models across development, staging, and production environments.</li><li>Design scalable inference and serving patterns for variable workloads.</li><li>Build and maintain automated data pipelines, ETL/ELT workflows, and data processing frameworks for AI/ML consumption.</li><li>Ensure data quality, lineage, governance, and versioning to support dependable model training and inference.</li><li>Work with structured and unstructured datasets across data lakes, data warehouses, and operational systems.</li><li>Deliver analytics-ready datasets to downstream systems and applications.</li><li>Lead multiple AI initiatives in parallel, including planning, execution, and coordination with internal teams and stakeholders.</li><li>Work closely with product, engineering, data, and business teams to deliver production-ready AI capabilities.</li><li>Contribute to architecture decisions, technical documentation, best practices, and engineering standards.</li><li>Support knowledge sharing, technical leadership, and continuous improvement across the AI function. 
</li></ul> 
 
Requirements<ul><li> 
Bachelor&rsquo;s degree in Computer Engineering, Computer Science, Artificial Intelligence, Data Science, or a related field.</li><li>5+ years of hands-on experience in AI engineering, machine learning engineering, MLOps, or data/ML platform engineering.</li><li>Proven experience deploying production AI/ML solutions in enterprise environments.</li><li>Strong programming experience in Python and SQL.</li><li>Strong experience with enterprise AI/ML architecture and delivery.</li></ul></div>

AI Architect - Riyadh,KSA

COZMALABS

We are looking for an experienced  Data Engineer to join our team full-time. This role requires a strong background in building and optimizing data pipelines, with a focus on supporting AI-driven solutions. The candidate will work closely with software engineers to ensure reliable data flows for AI models and contribute to cloud-based data integration using Microsoft Azure.Key Responsibilities:<ul><li>Design, develop, and maintain scalable data pipelines and ETL processes to support AI-driven projects</li><li>Build and optimize data infrastructure to ensure efficient data management, processing, and storage in cloud environments (Azure)</li><li>Collaborate with AI engineers to facilitate data preparation for machine learning models and AI solutions</li><li>Ensure data pipelines are secure, reliable, and meet performance standards</li><li>Monitor and troubleshoot data pipeline performance to ensure timely and accurate data delivery</li><li>Implement data governance policies and ensure compliance with data security standards</li></ul>Required Qualifications:<ul><li>Proven experience as a Data Engineer, with a focus on building and maintaining complex data pipelines for AI projects</li><li>Proficiency in SQL and experience with relational databases (e.g., PostgreSQL, SQL Server) and NoSQL databases (e.g., MongoDB)</li><li>Expertise in cloud-based data solutions, specifically Microsoft Azure (e.g., Azure Data Factory, Azure Synapse, Azure Databricks)</li><li>Experience with data integration tools and ETL frameworks (e.g., Apache Airflow)</li><li>Proficiency in at least one programming language for data manipulation (e.g., Python)</li><li>Knowledge of data warehousing solutions and best practices</li><li>Experience in working with data pipelines for AI or machine learning workflows</li><li>Strong problem-solving and analytical skills</li><li>Bachelor&rsquo;s degree in Computer Science, Engineering, or a related field</li><li>Proficiency in English (written and spoken)</li></ul>Desirable Qualifications:<ul><li>Familiarity with machine learning models and data preprocessing for AI solutions</li><li>Knowledge of containerization tools (Docker, Kubernetes) for data pipeline deployments</li></ul>Work Location: In person

Data Engineer

The Home Depot

<div>
 Req178618
 
 
 
 Position Purpose
 
 
 
 The Software Engineer II is responsible for independently developing and assisting in the design of a product that our customers and associates love. As a Software Engineer II, you will be part of a dynamic team with engineers of all experience levels who help each other build and grow technical and leadership skills while creating, deploying, and supporting production applications. In addition, Software Engineer II may be involved in configuration, security, resilience, performance tuning and production monitoring.
 
 
 We are dedicated to pushing the boundaries of technology to create solutions that make a difference to the company. Our team is looking for a talented AI/ML developer to join us developing cutting-edge machine learning models and applications for the Company.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 60% Delivery and Execution &ndash; Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable software solutions; Documents, reviews and ensures that all quality and change control standards are met; Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable; Writes custom code or scripts to automate infrastructure, monitoring services, and test cases; Writes custom code or scripts to do destructive testing to ensure adequate resiliency in production; Program configuration/modification and setup activities on large projects using HD approved methodology; Configures commercial off the shelf solutions to align with evolving business needs Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively
 </li>
 <li>
 20% Learning &ndash; Actively seeks ways to grow and be challenged using both formal and informal development channels; Learns through successful and failed experiment when tackling new problems
 </li>
 <li>
 20% Plans and Aligns &ndash; Collaborates with other team members in agile processes; Assists in creating new and better ways for the team to be successful; Relates openly and comfortably with diverse groups of people; Builds partnerships and works collaboratively with others to meet shared objectives
 </li>
 <li>
 Design and develop machine learning models and algorithms to address business challenges.
 
 
 </li>
 </ul>
 
 Direct Manager/Direct Reports
 
 
 
 <ul>
 <li>
 This position typically repots to Software Engineer Manager or Sr. Manager
 </li>
 <li>
 This position has 0 Direct Reports
 
 
 </li>
 </ul>
 
 Travel Requirements
 
 
 
 <ul>
 <li>
 No travel required.
 
 
 </li>
 </ul>
 
 Physical Requirements
 
 
 
 <ul>
 <li>
 Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.
 
 
 </li>
 </ul>
 
 Working Conditions
 
 
 
 <ul>
 <li>
 Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.
 
 
 </li>
 </ul>
 
 Minimum Qualifications
 
 
 
 <ul>
 <li>
 Must be eighteen years of age or older.
 </li>
 <li>
 Must be legally permitted to work in the United States.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 1-3 years of relevant work experience
 </li>
 <li>
 Extensive experience in AI, machine learning.
 </li>
 <li>
 Strong understanding of AI use cases and their application in business contexts.
 </li>
 <li>
 Proven track record of developing AI use case from concept to launch.
 </li>
 <li>
 Strong programming skills in Golang, Python.
 </li>
 <li>
 Experience building and maintaining robust data pipelines, ETL/ELT workflows, and data ingestion processes.
 </li>
 <li>
 Solid understanding of machine learning algorithms and principles.
 </li>
 <li>
 Experience in Google Cloud Platform and AI/ML related components such as Vertex AI, BigQueryML, and AutoML
 </li>
 <li>
 Experience with data preprocessing, data ingestion and model evaluation.
 </li>
 <li>
 Strong problem-solving skills and ability to work independently and as part of a team.
 </li>
 <li>
 Experience in writing SQL queries against a relational database
 </li>
 <li>
 Experience in version control systems and collaborative development tools.
 </li>
 <li>
 Experience with cloud platforms (Azure, GCP)
 </li>
 <li>
 Experience in source code version control (e.g. Git)
 </li>
 <li>
 Experience in Relational or NoSQL database technology
 </li>
 <li>
 Experience in cloud computing techniques
 </li>
 <li>
 Experience in CI/CD tools
 </li>
 <li>
 Experience in microservice-based architecture
 </li>
 <li>
 Experience with modern debugging and root cause analysis techniques
 </li>
 <li>
 Exposure to security frameworks for user and services authorization and authentication
 </li>
 <li>
 Exposure to creating and executing unit, functional, destructive and performance tests
 </li>
 <li>
 Excellent communication skills to convey technical concepts to non-technical stakeholders.
 </li>
 <li>
 Experience in REST and effective web service design
 </li>
 <li>
 Experience in production systems design including High Availability, Disaster Recovery, Performance, Efficiency, and Security
 
 
 </li>
 </ul>
 
 Minimum Education
 
 
 
 <ul>
 <li>
 The knowledge, skills and abilities typically acquired through the completion of a bachelor&rsquo;s degree program or equivalent degree in a field of study related to the job.
 
 
 </li>
 </ul>
 
 Minimum Years Of Work Experience
 
 
 
 <ul>
 <li>
 2-3+ years
 
 
 </li>
 </ul>
 
 Competencies
 
 
 
 <ul>
 <li>
 Global Perspective
 </li>
 <li>
 Manages Ambiguity
 </li>
 <li>
 Nimble Learning
 </li>
 <li>
 Self-Development
 </li>
 <li>
 Collaborates
 </li>
 <li>
 Cultivates Innovation
 </li>
 <li>
 Situational Adaptability
 </li>
 <li>
 Communicates Effectively
 </li>
 <li>
 Drives Results
 </li>
 <li>
 Interpersonal Savvy
 </li>
 </ul>
</div>

AI/ML Developer II (Remote)

Interact Technology Solutions

<div>
 
 Job Summary
 
 
 
 The
 
 Senior AI Data Engineer
 
 is responsible for designing, building, and optimizing enterprise-scale data and AI infrastructure to support machine learning models, generative AI applications, and real-time analytics. The role drives the development of end-to-end data pipelines, from ingestion to production-ready AI data products, ensuring scalability, performance, and compliance across multi-cloud environments.
 
 
 
 Accountability &amp; Responsibilities
 
 
 
 <ul>
 <li>
 Design, build, and maintain scalable ETL/ELT data pipelines using modern data engineering tools (e.g., Apache Spark, dbt).
 </li>
 <li>
 Architect and implement Lakehouse data platforms (Delta Lake, Apache Iceberg, Apache Hudi) following Medallion architecture (Bronze/Silver/Gold).
 </li>
 <li>
 Develop real-time streaming pipelines using Apache Kafka, Apache Flink, and Spark Structured Streaming.
 </li>
 <li>
 Build and optimize AI/GenAI data pipelines for LLM training, fine-tuning, and inference (tokenization, dataset curation, prompt engineering datasets).
 </li>
 <li>
 Design and implement Retrieval-Augmented Generation (RAG) pipelines, including embedding workflows and vector database integration.
 </li>
 <li>
 Manage feature stores for real-time and batch machine learning use cases.
 </li>
 <li>
 Integrate data pipelines with AI/ML platforms (Databricks MLflow, Azure ML, AWS SageMaker, Vertex AI, OpenAI/Azure OpenAI).
 </li>
 <li>
 Implement data orchestration workflows using Apache Airflow or similar tools with CI/CD pipelines.
 </li>
 <li>
 Ensure data quality, governance, and security using frameworks such as Great Expectations and data catalog tools.
 </li>
 <li>
 Deploy and manage infrastructure using Infrastructure-as-Code tools (Terraform, Bicep, CDK).
 </li>
 <li>
 Collaborate with Data Scientists, ML Engineers, and Solution Architects to deliver production-ready AI solutions.
 </li>
 <li>
 Lead technical design decisions, mentor junior engineers, and contribute to data platform strategy.
 </li>
 <li>
 Maintain documentation, data contracts, and operational runbooks for all pipelines.
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 
 1
 
 &ndash; Required Experience
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Engineering, or related field.
 </li>
 <li>
 4&ndash;5 years of experience in data engineering, with strong exposure to AI/ML data infrastructure.
 </li>
 <li>
 Proven experience building scalable data pipelines and working with large-scale datasets.
 </li>
 <li>
 Hands-on experience with AI/ML platforms and modern data architectures.
 </li>
 <li>
 Experience in regulated industries (e.g., Banking, Telecom, Healthcare) is a plus.
 </li>
 <li>
 Strong problem-solving, analytical thinking, and communication skills.
 </li>
 <li>
 Experience working in cross-functional teams and agile environments.
 
 
 </li>
 </ul>
 
 2&ndash; Technical Skills
 
 
 
 <ul>
 <li>
 Strong SQL and advanced data modeling techniques
 </li>
 <li>
 Apache Spark (PySpark, Spark SQL, Streaming)
 </li>
 <li>
 Python (pandas, PySpark, data processing libraries)
 </li>
 <li>
 Data pipeline orchestration (Apache Airflow)
 </li>
 <li>
 CI/CD for data pipelines (GitHub Actions / Azure DevOps)
 </li>
 <li>
 Lakehouse architectures (Delta Lake / Iceberg / Hudi)
 </li>
 <li>
 Streaming technologies (Kafka, Flink)
 </li>
 <li>
 Cloud platforms (AWS / Azure / GCP)
 </li>
 <li>
 Vector databases (Pinecone, Weaviate, pgvector, OpenSearch)
 </li>
 <li>
 RAG pipeline design and LLM data processing
 </li>
 <li>
 Infrastructure-as-Code (Terraform / Bicep / CDK)
 </li>
 <li>
 Containers (Docker, Kubernetes)
 </li>
 <li>
 Data quality &amp; governance tools
 </li>
 </ul>
</div>

Senior AI Data Engineer

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10000 machine learning infrastructure engineer jobs Near Me

Backend AI & Data Pipeline Engineer

Machine Learning Engineer - Drug Discovery

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AI Architect - Riyadh,KSA

Data Engineer

AI/ML Developer II (Remote)

Senior AI Data Engineer