mlops engineer

Version 1

<div><b>Company Description</b><p><br>
Version 1 has celebrated over 29 years in Technology Services and continues to be trusted by global brands to deliver solutions that drive customer success. Our expertise enables our customers to navigate the rapidly changing Digital-First world we live in. We foster strong partnerships with leading technology giants including Microsoft, AWS, Oracle, Red Hat, OutSystems, Snowflake, ensuring that our customers are provided with the highest quality solutions and services.</p><p>
We&rsquo;re an award-winning employer reflecting how our employees are at the very heart of Version 1 and what we do:</p><ul><li>
UK &amp; Ireland's premier AWS, Microsoft &amp; Oracle partner</li><li>
3300+ strong, &euro;350/&pound;300m revenue business</li><li>
10+ years as a Great Place to Work in Ireland &amp; UK</li><li>
Best Workplace for Women in the UK &amp; Ireland by GPTW</li><li>
Best Workplace for Wellbeing in the UK by GPTW</li></ul><p>
We&rsquo;re a core values driven company, we hire people who share our values, and we reward those who display and foster them, it&rsquo;s deeply embedded within our DNA. Invest in us and we&rsquo;ll invest in you!</p><b><br>
Job Description</b><p><b><br>
About the Role</b></p><p>
We are looking for an experienced <b>AWS MLOps / DevOps Engineer</b> to design, automate, and optimize machine learning and data workflows on AWS. You will play a central role in building scalable, secure, and reliable ML infrastructure that accelerates model development and deployment across the organization.</p><p>
This position is ideal for someone who combines strong <b>AWS engineering</b>, <b>modern DevOps practices</b>, and <b>hands-on ML workflow experience</b>.</p><p><b>
Key Responsibilities</b></p><p><b>
MLOps &amp; Machine Learning Workflow Automation</b></p><ul><li>
Build and maintain end-to-end <b>ML pipelines</b> using Amazon SageMaker, Step Functions, Lambda, and Glue.</li><li>
Implement model lifecycle workflows: <b>data preprocessing, feature engineering, model training, model registry, deployment, and monitoring</b>.</li><li>
Automate model deployment to real-time endpoints and batch systems.</li><li>
Establish <b>model monitoring</b>, including data drift, model drift, performance metrics, and automated retraining triggers.</li></ul><p><b>
AWS Cloud Engineering</b></p><ul><li>
Design, automate, and maintain AWS infrastructure using <b>AWS CloudFormation</b> and/or Terraform.</li><li>
Build scalable data and ML environments using AWS services such as S3, ECR, ECS/EKS, Lambda, VPC, IAM, and CloudWatch.</li><li>
Build Spark-based ETL pipelines using <b>AWS Glue, EMR, or Spark on Kubernetes</b>.</li><li>
Ensure compliance with AWS security best practices including IAM governance and encryption.</li></ul><p><b>
DevOps, CI/CD &amp; Automation</b></p><ul><li>
Develop CI/CD pipelines for ML and data workflows using GitHub Actions, GitLab CI, Jenkins, or CodePipeline.</li><li>
Implement automated testing for data validation, model quality, pipeline integrity, and infrastructure deployments.</li><li>
Maintain logging, monitoring, and observability systems using CloudWatch, Prometheus/Grafana, or ELK.</li></ul><p><b>
Collaboration &amp; Technical Leadership</b></p><ul><li>
Work closely with Data Scientists to productionize notebooks, scripts, and models.</li><li>
Collaborate with Data Engineering to align data processing workflows with ML requirements.</li><li>
Drive best practices in automation, model deployment, and cloud architecture.</li></ul><br>
<b>
Qualifications</b><p><b><br>
Required Skills &amp; Experience</b></p><p><b>
Technical Expertise</b></p><ul><li>
Strong hands-on experience with <b>AWS CloudFormation</b> for Infrastructure-as-Code.</li><li>
Deep expertise with <b>Amazon SageMaker</b> (training jobs, pipelines, model registry, endpoints).</li><li>
Competency with <b>AWS Glue and Spark</b> for ETL and feature pipeline development.</li><li>
Strong programming skills in <b>Python</b> (ML workflows, pipeline orchestration, automation).</li><li>
Proficient with <b>AWS Lambda</b> for serverless ML and automation.</li><li>
Experience with building <b>data preprocessing</b> workflows and <b>feature engineering</b> pipelines.</li><li>
Strong understanding of <b>model monitoring, model drift detection, and model productionisation</b>.</li></ul><p><b>
DevOps &amp; Cloud</b></p><ul><li>
Expertise in CI/CD for ML (GitHub Actions, GitLab, Jenkins, CodePipeline).</li><li>
Familiarity with Docker and container orchestration (ECS, EKS, Fargate).</li><li>
Strong understanding of AWS networking, IAM, security best practices, and high-availability design.</li></ul><p><b>
Soft Skills</b></p><ul><li>
Strong communication skills and ability to collaborate with cross-functional teams.</li><li>
Ability to lead technical discussions and influence architectural decisions.</li><li>
Problem-solving mindset with passion for automation and scalability.</li></ul><p><b>
Preferred Qualifications</b></p><ul><li>
Experience with MLflow, Kubeflow, Airflow, or Step Functions for pipeline orchestration.</li><li>
Experience with Glue Studio, Glue Workflows, or EMR for large-scale Spark processing.</li><li>
AWS Certifications (MLOps / DevOps Engineer / Solutions Architect / ML Specialty).</li><li>
Knowledge of data governance, lineage, and compliance frameworks.</li><li>
Knowledge of security best practices</li></ul><p><b>
Nice to have</b></p><ul><li>
Finance Sector customer experience such as banking, insurance, etcetera</li><li>
Understanding of regulatory constraints, auditing requirements, compliance, and secure data-handling patterns.</li></ul><br>
<b>
Additional Information</b><p><b><br>
Why Version 1?</b></p><p>
At Version 1, we believe in providing our employees with a comprehensive benefits package that prioritises their wellbeing, professional growth, and financial stability.</p><ul><li>
Share in our success with our Quarterly Performance-Related Profit Share Scheme, where employees collectively benefit from a share of our company's profits.</li><li>
Strong Career Progression &amp; mentorship coaching through our Strength in Balance &amp; Leadership schemes with a dedicated quarterly Pathways Career Development programme.</li><li>
Flexible/remote working, Version 1 is tremendously understanding of life events and people&rsquo;s individual circumstances and offer flexibility to help achieve a healthy work life balance.</li><li>
Financial Wellbeing initiatives including; Pension, Private Healthcare Cover, Life Assurance, Financial advice and an Employee Discount scheme.</li><li>
Employee Wellbeing schemes including Gym Discounts, Bike to Work, Fitness classes, Mindfulness Workshops, Employee Assistance Programme and much more. Generous holiday allowance, enhanced maternity/paternity leave, marriage/civil partnership leave and special leave policies.</li><li>
Educational assistance, incentivised certifications, and accreditations, including AWS, Microsoft, Oracle, and Red Hat.</li><li>
Reward schemes including Version 1&rsquo;s Annual Excellence Awards &amp; &lsquo;Call-Out&rsquo; platform.</li><li>
Environment, Social and Community First initiatives allow you to get involved in local fundraising and development opportunities as part of fostering our diversity, inclusion and belonging schemes.</li></ul><p>
And many more exciting benefits&hellip; drop us a note to find out more.</p></div>

MLOps Engineer

NXT LABS

<div>
 Job Brief
 <p>
  <strong>
   Position:
  </strong>
  Senior MLOps Engineer
 </p>
 Role Objective
 <p>
  Ensure all production Machine Learning models are reliable, continuously monitored, retrainable, and fully auditable. This role exists to prevent silent model decay and reduce operational load on Data Science teams by architecting robust, automated deployment and monitoring frameworks.
 </p>
 Role Description
 <p>
  The Senior MLOps Engineer is responsible for the technical implementation of production-grade Machine Learning systems. This includes architecting scalable pipelines, managing real-time inference services, and ensuring seamless integration within cloud environments.
 </p>
 <p>
  The role focuses on transitioning models from experimentation to high-throughput production services, ensuring each solution is secure, performant, and engineered to solve complex organizational challenges at scale.
 </p>
 Company Overview
 <p>
  <strong>
   NXT
  </strong>
  is a fast-growing technology organization focused on building scalable products and intelligent solutions that simplify and improve the lives of users worldwide. We aim to foster a high-performance technology culture and an entrepreneurial ecosystem recognized for excellence and innovation.
 </p>
 Key Responsibilities
 <ul>
  <li>
   <p>
    <strong>
     End-to-End Productionization:
    </strong>
    Architect and deploy ML models across the full lifecycle (training &rarr; deployment &rarr; monitoring).
   </p>
  </li>
  <li>
   <p>
    <strong>
     Model Observability:
    </strong>
    Implement comprehensive monitoring for data drift, model drift, and performance degradation.
   </p>
  </li>
  <li>
   <p>
    <strong>
     Retraining &amp; Rollbacks:
    </strong>
    Design and own automated retraining pipelines and safe rollback mechanisms to maintain model integrity.
   </p>
  </li>
  <li>
   <p>
    <strong>
     ML CI/CD:
    </strong>
    Build and maintain automated workflows for model versioning, lineage tracking, and artifact storage.
   </p>
  </li>
  <li>
   <p>
    <strong>
     Inference Optimization:
    </strong>
    Apply optimization techniques such as quantization, pruning, and caching to reduce compute costs and improve latency.
   </p>
  </li>
  <li>
   <p>
    <strong>
     Standardization:
    </strong>
    Establish best practices for model registries, metadata management, and reproducibility of production assets.
   </p>
  </li>
  <li>
   <p>
    <strong>
     Operational Integration:
    </strong>
    Collaborate with cross-functional teams to operationalize ML models for use cases such as search ranking, valuation engines, and quality scoring.
   </p>
  </li>
 </ul>
 Technical Stack &amp; Tools
 <ul>
  <li>
   <p>
    <strong>
     Programming:
    </strong>
    Python (Expert), SQL (Expert), FastAPI or Flask (Model APIs)
   </p>
  </li>
  <li>
   <p>
    <strong>
     Orchestration:
    </strong>
    Airflow, Dagster, or Prefect; GitHub Actions for CI/CD
   </p>
  </li>
  <li>
   <p>
    <strong>
     MLOps Frameworks:
    </strong>
    MLflow, DVC, Weights &amp; Biases, Kubeflow
   </p>
  </li>
  <li>
   <p>
    <strong>
     Infrastructure:
    </strong>
    Docker, Kubernetes
   </p>
  </li>
  <li>
   <p>
    <strong>
     Cloud Platforms:
    </strong>
    AWS SageMaker, Azure ML, or GCP Vertex AI
   </p>
  </li>
  <li>
   <p>
    <strong>
     Monitoring:
    </strong>
    Prometheus, Grafana, or equivalent observability tools
   </p>
  </li>
  <li>
   <p>
    <strong>
     Data Processing:
    </strong>
    Apache Spark (PySpark), Databricks, or Snowflake
   </p>
  </li>
 </ul>
 Qualifications &amp; Experience
 <ul>
  <li>
   <p>
    <strong>
     Experience:
    </strong>
   </p>
   <ul>
    <li>
     <p>
      5+ years in Data Science / Machine Learning
     </p>
    </li>
    <li>
     <p>
      3+ years specifically in MLOps or Production ML Engineering
     </p>
    </li>
   </ul>
  </li>
  <li>
   <p>
    <strong>
     Production Delivery:
     <br/>
    </strong>
    Proven experience building, deploying, and maintaining high-throughput inference services in live production environments.
   </p>
  </li>
  <li>
   <p>
    <strong>
     System Grit:
     <br/>
    </strong>
    Comfortable working with legacy systems and imperfect infrastructure to design robust, scalable pipelines.
   </p>
  </li>
  <li>
   <p>
    <strong>
     Business Systems Integration:
     <br/>
    </strong>
    Experience integrating ML solutions with enterprise systems such as ERP, CRM, or supply chain platforms.
   </p>
  </li>
  <li>
   <p>
    <strong>
     Cloud Collaboration:
     <br/>
    </strong>
    Strong experience delivering AI solutions on AWS, Azure, or GCP in collaboration with Data Engineering and DevOps teams.
   </p>
  </li>
 </ul>
 Additional Notes
 <ul>
  <li>
   <p>
    <strong>
     Immediate joiners will be preferred.
    </strong>
   </p>
  </li>
 </ul>
</div>


Senior MLOps Engineer

ImagineArt

<div><p><b>Role Overview:
</b><br>
<br>
Support the deployment, scaling, optimization, and monitoring of AI/ML models in production environments. Work closely with data scientists and developers to ensure models run efficiently, reliably, and with fast inference performance.</p><p><b>
Key Responsibilities</b></p><ul><li>Develop, maintain, and deploy ML/AI models into production environments.</li><li>Build and serve model inference APIs using frameworks like FastAPI.</li><li>Optimize models for better inference performance including quantization and model compression.</li><li>Package and containerize models using Docker and manage deployments with orchestration tools (e.g., Kubernetes).</li><li>Set up CI/CD pipelines and automation workflows for model deployment.</li><li>Monitor model performance, latency, and reliability in production.</li><li>Troubleshoot and resolve deployment, infrastructure, or inference issues.</li><li>Collaborate with ML Engineers, Data Scientists, and DevOps teams to streamline workflows.</li></ul><p><b>
Skills &amp; Requirements</b></p><ul><li>Proficiency in Python and familiarity with building REST APIs (FastAPI, Flask).</li><li>Experience deploying ML models and serving them reliably.</li><li>Understanding of model optimization techniques such as quantization for faster inference.</li><li>Knowledge of Docker and container orchestration (e.g., Kubernetes).</li><li>Familiarity with CI/CD tools and automation workflows.</li><li>Ability to monitor and troubleshoot production systems.</li></ul><p><b>
Nice to Have</b></p><ul><li>Experience with model versioning and ML lifecycle tools.</li><li>Exposure to any cloud platform (AWS, GCP, Azure).</li><li>Understanding of performance profiling and benchmarking tools.</li></ul></div>

Associate MLOps Engineer

AstraZeneca

<div><h1 class="jobSectionHeader"><b>Job Title: Analyst &ndash; MLOps Data Engineer</b></h1><h2 class="jobSectionHeader"><b>
Grade: C3</b></h2><p>
Shift: 2 pm to 11 pm IST</p><p>
Role: Individual contributor role.</p><p>
Location: Manyata Tech Park, Bangalore.</p><h2 class="jobSectionHeader"><b>
Introduction to role:</b></h2><p>
The Data Engineer/MLOps role collaborates with data scientists, analysts, and commercial operations to design, deploy, and manage machine learning systems that enhance sales effectiveness and engagement. This position is responsible for ensuring that models are reproducible, compliant, performant, and scalable throughout their lifecycle&mdash;from experimentation to production. A strong focus is placed on data quality, monitoring, and governance.</p><h2 class="jobSectionHeader"><b>
Accountabilities:</b></h2><ul><li>
Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production, including packaging, CI/CD, automated testing, and deployment across multiple environments.</li><li>
Data Pipeline Development: Design robust batch and streaming data workflows; define and manage feature sets, lineage, and reuse to support AI/ML initiatives.</li><li>
Production Operations: Ensure reliability and scalability of ML systems; manage incident response, on-call support, and implement effective logging, tracing, and alerting.</li><li>
Monitoring &amp; Observability: Establish comprehensive monitoring for model performance, data drift, bias, and service health; set thresholds, create dashboards, and automate remediation processes.</li><li>
Model Governance &amp; Compliance: Implement version control, approvals, documentation, and audit trails for datasets, code, models, and experiments; ensure compliance with privacy regulations (HIPAA/PHI, GDPR) and support AZ IT compliance requirements as needed.</li><li>
Experiment Management: Standardize experiment tracking and artifact management; promote guidelines for feature engineering, model packaging, and dependency management.</li><li>
Release &amp; Change Management: Coordinate releases with commercial operations and IT; maintain runbooks, rollback strategies, change tickets, and release notes in alignment with enterprise processes.</li><li>
Security &amp; Access Controls: Enforce secrets management, role-based access control, network policies, and data protection for sensitive healthcare and commercial data.</li><li>
Cost Optimization: Monitor and optimize cloud and computing costs for training, inference, and data movement; select architectures that balance performance with budget constraints.</li><li>
Collaboration &amp; Enablement: Work closely with data scientists and business stakeholders; provide frameworks, templates, and guardrails; conduct training and code reviews to support engineering best practices.</li><li>
Documentation &amp; Knowledge Sharing: Develop clear technical documentation, operational playbooks, and user guides for models, pipelines, and platform components.</li></ul><h2 class="jobSectionHeader"><b>
Essential Skills/Experience:</b></h2><ul><li>
Education: Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Engineering, or a related field, or equivalent experience.</li><li>
Experience: 3&ndash;6+ years in MLOps, Data Engineering, or ML Platform roles, with a proven track record of deploying ML solutions at scale.</li><li>
Programming: Proficiency in Python; familiarity with SQL; experience with unit and integration testing, and code quality standards.</li><li>
CI/CD &amp; Infrastructure: Experience with CI/CD tools (e.g., GitHub Actions, Azure DevOps) and containerization (Docker).</li><li>
ML Tools: Hands-on experience with model packaging and serving frameworks (e.g., SageMaker, Azure ML), and experiment tracking tools.</li><li>
Data Technologies: Proficiency with distributed processing (Spark), data orchestration (Airflow), and cloud data services (e.g., Azure Data Lake, Snowflake, AWS S3).</li><li>
Security &amp; Compliance: Understanding data privacy and security in healthcare; experience with secrets management and audit controls.</li></ul><h2 class="jobSectionHeader"><b>
Desirable Skills/Experience:</b></h2><ul><li>
Domain Experience: Knowledge of pharmaceutical commercial analytics (HCP/HCO targeting, call planning, incentive compensation, demand forecasting, omnichannel measurement).</li><li>
Performance &amp; Scalability: Experience with high-throughput inference, batch scoring at scale, and low-latency APIs.</li><li>
Workflow Reliability: Skills in incident management and capacity planning for ML systems.</li><li>
Automation &amp; Templates: Ability to create reusable pipelines and starter kits for rapid project onboarding.</li><li>
Communication: Excellent verbal and written communication skills; able to present complex findings to both technical and non-technical audiences.</li><li>
Team Collaboration: Strong orientation toward teamwork and cross-functional collaboration.</li><li>
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.</li></ul><p></p><p>
At AstraZeneca's Alexion division, we champion diversity and foster an energizing culture where new ideas thrive. Our commitment to inclusion ensures that life-changing innovations can come from anywhere. We celebrate each other's successes and take pride in giving back to our communities. Here, your career is more than just a path; it's a journey to making a difference where it truly counts.</p><p></p><p>
Ready to make a difference? Apply now to join our team!</p><p></p><p>
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.</p></div>

Analyst – MLOps Data Engineer

Fractal Analytics

<div><div>It's fun to work in a company where people truly BELIEVE in what they are doing!</div><div></div><div><i>
We're committed to bringing passion and customer focus to the business.</i></div><div></div><div>
Role: MLOps Platform Engineer</div><div>
Experience: +5 years</div><div>
Overview: We are seeking a skilled MLOps Platform Engineer to build and maintain a robust platform for the entire Machine Learning (ML) lifecycle. This role involves automating ML model training, testing, deployment, and monitoring, integrating seamlessly with our existing infrastructure to accelerate ML innovation.</div><div>
Key responsibilities:</div><ul><li><div>
CI/CD for ML: Develop and manage CI/CD pipelines for ML model code and infrastructure, covering unit, integration, and deployment to all environments.</div></li><li><div>
Automated ML Training: Design and implement pipelines for repeatable model training, automatic sweeps, data processing, and hyperparameter optimization.</div><ul><li><div>
Include scheduling, queuing, and cost monitoring for training runs.</div></li><li><div>
Support training on cloud and on-premise GPU resources.</div></li></ul></li><li><div>
Model Monitoring: Establish tools and dashboards for continuous monitoring of deployed ML models, ensuring health, availability, and performance.</div></li><li><div>
Platform Integration: Ensure the MLOps platform integrates with company workflows, data pipelines, and computing architecture.</div></li><li><div>
MLOps Best Practices: Promote and implement best practices for reproducibility, version control, and governance across the ML lifecycle.</div></li></ul><div>
Skills:</div><ul><li><div>
Strong experience MLOps, DevOps, or ML Engineering, focusing on ML infrastructure</div></li><li><div>
Programming: Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).</div></li><li><div>
CI/CD &amp; Orchestration: Experience with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions) and workflow orchestrators (e.g., Airflow).</div></li><li><div>
Model Serving &amp; De</div></li></ul><div></div><div>
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!</div><div></div><div><div>
Not the right fit? Let us know you're interested in a future opportunity by clicking <i>Introduce Yourself</i> in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!</div></div></div>

ML Ops Engineer

Anblicks

<div></div><div><div><b>Role: ML Engineer(Databricks MLOps)</b></div><div><b>
Location: Hyderabad</b></div><div><b>
Experince: 8+ Yrs</b></div><div></div><div><b>
Job Description Summary</b></div><div>
ML Engineer within the Data Science and Machine Learning team leverages and third-party software to create solutions to business problems defined by specific business requirements. In this position, you will draw upon technical, AI/ML engineering, Data and MLOps experience to solve complex marketing analytics problems on very large volumes of data.
</div><div>
</div><div>As a Machine Learning (ML) Engineer You will build and operationalize machine learning pipelines involving terabytes of data. You will have the responsibility to help define requirements, create software designs, implement code to these specifications, As an ML Engineer, you will be expected to:</div><div>
</div><div>
Develop and maintain a comprehensive enterprise architecture for AI/ML/GenAI initiatives, ensuring alignment with overall business strategy and technology roadmap.</div><div>
Architect hyperscale MLOps solutions and pipelines</div><div>
Work with Applied Scientists, Data Scientists, Product owners, ML Engineers, and Software Engineers to design and deliver ML solutions in production at scale.</div><div>
Develop automated AI and ML workflows and end-to-end pipelines for data preparation, training, deployment, monitoring, etc., and ensure the quality of architecture and design of our ML systems and data infrastructure.</div><div>
Leverage AI to develop GenAI powered solutions to complement our data science and product build capabilities</div><div>
Assess current state AI/ML/GenAI capabilities across various business domains, identify gaps, and design target state architectures to drive innovation, revenue growth and operational excellence.</div><div>
Lead transformational initiatives to bridge the gap between current and desired AI/ML capabilities, collaborating with cross-functional teams to ensure successful implementation.</div><div>
Establish governance frameworks and decision criteria for AI/ML and GenAI projects, ensuring adherence to industry standards, regulatory requirements, Responsible AI and architectural principles.</div><div>
Create and maintain reference architectures, patterns, and best practices for AI/ML lifecycle and integration within enterprise ecosystem.</div><div>
Lead the technology evaluation and process improvements to drive Experimentation, Model Development and ML Ops at scale</div><div>
Lead and drive standardization of LLM onboarding process, RAG pipelines and application development</div><div>
Conduct architecture reviews and risk assessments for proposed AI/ML solutions, ensuring they meet security, scalability, and interoperability requirements.</div><div>
Utilize advanced data science techniques (e.g., Natural Language Processing, clustering, predictive analytics, regression analyses, survival analysis, segmentation, and experimentation) to propose enhancements and innovations to business processes.</div><div>
Conduct sophisticated statistical analyses and maintain high reliability of machine learning pipelines in production environments, ensuring minimal downtime and optimal performance.</div><div>
Collaborate with business leaders and product to identify opportunities for AI/ML-driven innovation and guide the development of use cases that deliver tangible business value.</div><div>
Foster a culture of continuous learning and innovation in AI/ML practices across the enterprise architecture team and broader organization.</div><div>
</div><div><b>
BASIC QUALIFICATIONS</b></div><ul><li>8+ of years of experience in enterprise architecture, with a focus on AI/ML integration and transformation projects.
</li><li>6+ years professional experience in software development</li></ul><div>
Bachelor&rsquo;s Degree in Computer Science or Associate Degree &amp; 3+ years of development experience or equivalent experience
</div><ul><li>Computer Science fundamentals in object-oriented design</li><li>Computer Science fundamentals in data structures</li><li>Computer Science fundamentals in algorithm design, problem solving, and complexity analysis</li><li>Knowledge of, at least, one modern programming language such as Python, Java, C++, C, Java, Python or Perl</li></ul><div>
</div><div><b>
PREFERRED QUALIFICATIONS</b></div><div>
8+ years of experience architecting scalable ML infrastructure and big data systems.</div><div>
Databricks Architect Certification is required</div><div>
6+ years of architecting solutions using Databricks. Strong experience using Mosaic AI, Unity Catalogue, mlflow, workflow orchestration and other databricks native MLOps capabilities.</div><div>
At least 1+ year experience in GenAI (Technical familiarity with 2 or more) - OpenAI API, Bedrock API, Vertex API, LangGraph, other agentic frameworks</div><div>
High attention to detail and proven ability to manage multiple, competing priorities simultaneously.</div><div>
Experience MLOps and orchestration tools such as Airflow, Kubeflow, DAGster, Optuna, Mlflow or other similar MLOps tools.</div><div>
Experience with operationalizing and migrating ML models into production at scale.</div><div>
Developing Large scale model inference solutions using parallel execution framework using spark, EMR, databricks</div><div>
Experience developing complex orchestration and MLOps pipelines stitching together large volumes of data for training and scoring</div><div>
Experience with Large Language Models, fine tuning and deployment frameworks using HuggingFace capabilities or cloud provider solutions such as Amazon Bedrock, Vertex AI model garden etc.</div><div>
Familiarity with Vector databases such as Pinecone, ChromaDB or similar tools.</div><div>
Experience in CI/CD/DevOps, Deployment and Automation Tool &ndash; CI/CD, Jenkins, Terraform, Cloud Formation Template or similar</div><div>
Proficiency with Apache Spark, EMR/DataProc and Cloud based tools (Snowflake, Redshift, EMR, Glue, Step Functions, Lambda, Step functions, AWS Batch, or similar etc.).</div><div>
Experience with ML libraries like H20, scikit learn and deep learning frameworks (PyTorch, TensorFlow, etc.).</div><div>
Experience with end-to-end software development and life cycle of ML solutions.</div><div>
Excellence in technical communication with scientists and engineers.</div><div>
At least 2years Database (SQL) experience, Linux</div><div>
At least 6+ years of AWS infrastructure experience - Cloud run, App server, RDS, S3, EC2, EMR or equivalent GCP experience</div><div>
</div><div><b>
What will set you apart:</b></div><div>
Databricks Certification</div><div>
Langgraph, Databricks MLFlow experience, Docker experience, Kubernetes experience</div><div>
Knowledge of LLM observability platforms</div><div>
</div><div>
Good communication skills: communicate ideas clearly and effectively to other members of the analytics team and to the client at multiple levels (both technical and business)</div><div>
Analytic problem-solving skills with the ability to think outside-the-box</div><div>
Analytical thinker that excels at analyzing and understanding data to answer questions</div><div>
Excellent understanding of data concepts, data architecture, data manipulation/engineering, and data engineering design</div><div>
Passion for considering how projects fit into the wider business picture</div><div>
An understanding in multiple types of programming languages in order to be adaptable (statically typed vs. dynamically typed and object-oriented vs. procedural)</div><div>
Self-Starter &ndash; Able to work independently with little guidance</div><div>
Adaptable - Able to adapt to diverse technical challenges and systems</div><div>
Ability to formulate and present insights with gathered data to both technical and non-technical peers, leaders, and clients</div></div><div></div>

ML Engineer(Databricks MLOps)

International Turnkey Systems - ITS

<div>
 <p>
  For a
  <strong>
   well-known Islamic Bank in Bahrain
  </strong>
  , we are looking for an experienced
  <strong>
   AI/ML Specialist (Engineer &ndash; Data Platform)
  </strong>
  . This role is focused on designing, developing, and deploying advanced machine learning solutions to support digital transformation initiatives in the banking sector. The successful candidate will bring deep expertise in ML, NLP, cloud AI platforms, and MLOps, with a strong background in financial services.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Machine Learning &amp; AI Solutions
  </strong>
 </p>
 <ul>
  <li>
   Design and develop ML models for banking use cases (fraud detection, credit scoring, churn prediction, personalization).
  </li>
  <li>
   Collaborate with data engineers to prepare and preprocess data.
  </li>
  <li>
   Evaluate and optimize models for accuracy, precision, and recall.
  </li>
  <li>
   Deploy models using MLOps frameworks and CI/CD pipelines.
  </li>
  <li>
   Ensure explainability, compliance, and auditability of models.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Core AI/NLP Engineering
  </strong>
 </p>
 <ul>
  <li>
   Work with Python frameworks (PyTorch, TensorFlow, LangChain, Hugging Face, OpenAI API, Anthropic Claude).
  </li>
  <li>
   Fine-tune LLMs (LoRA, PEFT, prompt tuning).
  </li>
  <li>
   Implement RAG pipelines and vector databases (Pinecone, FAISS, Weaviate, Chroma).
  </li>
  <li>
   Apply prompt engineering and orchestration tools (LangChain, LlamaIndex, Semantic Kernel).
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Software Engineering &amp; Backend Integration
  </strong>
 </p>
 <ul>
  <li>
   Develop APIs (RESTful, GraphQL), webhooks, and microservices.
  </li>
  <li>
   Containerize and deploy applications (Docker, Kubernetes).
  </li>
  <li>
   Integrate with enterprise systems (SharePoint, Salesforce, SQL).
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Agent Orchestration &amp; Conversational AI
  </strong>
 </p>
 <ul>
  <li>
   Utilize agent frameworks (LangGraph, AutoGen, CrewAI, Flowise).
  </li>
  <li>
   Design conversational UX with frameworks (Google CCAI, Microsoft Bot Framework, Voiceflow, Botpress).
  </li>
  <li>
   Conduct A/B testing and optimize user satisfaction.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Data &amp; Infrastructure
  </strong>
 </p>
 <ul>
  <li>
   Build pipelines with Airflow, dbt, Kafka.
  </li>
  <li>
   Manage feature stores and model registries (MLflow, Kubeflow).
  </li>
  <li>
   Monitor models for drift and performance issues.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Governance, Security &amp; Compliance
  </strong>
 </p>
 <ul>
  <li>
   Conduct bias/fairness audits.
  </li>
  <li>
   Ensure compliance with GDPR, ISO 42001, NIST AI RMF, and local banking regulations.
  </li>
  <li>
   Implement secure logging and audit trails.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Product Strategy &amp; Business Alignment
  </strong>
 </p>
 <ul>
  <li>
   Translate business problems into AI use cases.
  </li>
  <li>
   Define KPIs (accuracy, ROI, user satisfaction).
  </li>
  <li>
   Manage vendors and drive adoption of AI solutions.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Required Qualifications
  </strong>
 </p>
 <ul>
  <li>
   <strong>
    Education:
   </strong>
   Bachelor&rsquo;s Degree in Computer Science (BE in Computer Science &amp; Engineering).
  </li>
  <li>
   <strong>
    Experience:
   </strong>
   7&ndash;10 years in AI/ML engineering, preferably in banking and IT transformation.
  </li>
  <li>
   <strong>
    Professional Training:
   </strong>
   Machine Learning, Deep Learning, MLOps, AI in Financial Services.
  </li>
  <li>
   <strong>
    Certifications:
   </strong>
  </li>
  <li>
   Google Professional ML Engineer
  </li>
  <li>
   Microsoft AI Engineer Associate
  </li>
  <li>
   TensorFlow Developer Certificate
  </li>
  <li>
   AWS Certified Machine Learning &ndash; Specialty
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  Skills &amp; Competencies
 </p>
 <ul>
  <li>
   Machine Learning / Deep Learning
  </li>
  <li>
   Statistics &amp; Finance knowledge
  </li>
  <li>
   Data Preprocessing &amp; Feature Engineering
  </li>
  <li>
   Model Deployment &amp; MLOps
  </li>
  <li>
   Cloud AI Platforms (Azure ML, AWS SageMaker, Vertex AI)
  </li>
  <li>
   Explainable AI (XAI) &ndash; SHAP, LIME
  </li>
  <li>
   Statistical Analysis
  </li>
  <li>
   Business Problem Solving
  </li>
 </ul>
 <p>
 </p>
</div>


AI/ ML Specialist

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