Total 5 job vacancies by Unison Group

Responsibilities:

• Developing and maintaining the entire DevOps pipelines.

• Coordinate with the stakeholders from requirements gathering until successful implementation DevOps pipeline on boarding.

• Support developers in preparation of JIRA deployment tickets as part of change management

• Work with the vendors and for internal platforms to setup centralized DevOps solutions considering market best practice, Industry standard and ease of support.

• Work with the vendors and for internal platforms to upgrade the DevOps tools farm and integrating with tools.

• Lead analysis and resolution of root cause for Test Orchestration / Environment related issues

• Ability to multitask and work in a fast-paced, collaborative team environment.

• Excellent written and oral communication skills; writing, publishing and conference-level presentation skills

• Stays current with industry trends and leads development of key DevOps, Runtime, and Operational innovation platforms.

Requirements:

• Deep technical background, with hands-on experience in supporting CI/CD pipelines (Bitbucket, Jenkins, Artifactory, Sonar, Veracode for SAST, JIRA and Confluence).

• Hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions or similar tools is a must-have.

• Must have experience in containerizing applications and have hands-on with building pods / containers in Kubernetes

• Good Experience on OpenShift or AWS

• Prior technical background on different build and branching strategies.

• Deep technical background on Kubernetes deployment process.

• Good knowledge on maintaining/deploying secrets in Kubernetes.

• Prior working experience in setting up Jira workflow.

• Strong hands-on experience on Shell or Groovy scripting.

• Prior experience is customizing JIRA and Setting up confluence spaces.

• Prior hand-on experience in setting up CI/CD pipeline as a centralized solution.

• Hand on experience in setting up DevOps governance, Security standards and audit requirements.

• Prior experience in setting up DevOps pipeline in internal and external cloud offerings.

• Experience with continuous testing, techniques and tools (Junit, Sonar Cube, Veracode etc.)

• Technical exposure of automation accomplished with advanced Python, Chef, Ruby and PowerShell

• Experience in managing hybrid cloud/on-prem environments.

• Familiarity with observability platforms and incident response workflows.

• Working knowledge of AI / Machine Learning applications will be an advantage.

• Working knowledge of Big Data environment like Hadoop, Cloudera and ElasticSearch.

Technical Skills:

• CI/CD software, tools and process

o Bitbucket

o Jenkins

o JIRA

o Artifactory

o Confluence

o Sonar

o Veracode for SAST

We are looking for a skilled Data Engineer with strong experience in cloud platforms like Amazon Web Services and Microsoft Azure. The candidate will be responsible for building scalable data pipelines, managing large datasets, and supporting analytics and reporting systems.

Essential Technical Skills

• Data Engineering: Strong foundation in data engineering principles, ETL/ELT processes, and data pipeline design patterns
• PySpark: Proven hands-on experience developing data pipelines using PySpark, including DataFrames API, Spark SQL, and performance optimization
• Databricks Platform: Practical experience with Databricks workspace, cluster management, notebooks, and job orchestration
• Workspace AI Agent: Knowledge of Databricks Workspace AI Agent capabilities and integration
• Data Modelling: Experience implementing data models including dimensional modeling, data vault, or lakehouse architectures
• Delta Lake: Understanding of Delta Lake features including ACID transactions, schema evolution, and optimization techniques
• Python: Strong Python programming skills for data processing and automation

Additional Technical Skills

SQL proficiency for data querying and transformation

• Experience with cloud platforms (Azure, AWS, or GCP)
• Understanding of data governance and security best practices
• Knowledge of streaming data processing (Structured Streaming)
• Familiarity with DevOps practices and CI/CD pipelines
• Experience with version control systems (Git)
• Understanding of data quality frameworks and testing methodologies

Professional Experience

• Minimum 8 years in data engineering or related roles
• At least 2-3 years of hands-on experience with Databricks platform
• Proven track record of refactoring legacy code to modern frameworks
• Experience building and maintaining production data pipelines at scale
• Background working across multiple data sources and formats
• Experience in agile development environments

Required Certifications

• Databricks Certified Data Engineer Associate OR Databricks Certified Data Engineer Professional

Additional Certifications (Preferred)

• Databricks Certified Associate Developer for Apache Spark
• Cloud platform certifications (Azure Data Engineer Associate, AWS Certified Data Analytics, or Google Cloud Professional Data Engineer)
• Relevant data engineering or big data certifications

Soft Skills

• Strong problem-solving and analytical thinking abilities
• Excellent communication skills to explain technical concepts clearly
• Ability to work collaboratively in cross-functional teams
• Self-motivated with strong attention to detail
• Adaptable to changing priorities and technologies
• Client-focused mindset with commitment to quality delivery

Job Summary:
We are seeking a skilled ML Ops / AI Integration Engineer to build, integrate, and manage Generative AI and Agentic AI solutions within enterprise environments. The ideal candidate will work closely with data scientists, architects, and DevOps teams to design, deploy, and optimize AI pipelines, ensuring scalable, secure, and reliable AI systems. This role focuses on automation, cloud integration, and production deployment of AI models.

Key Responsibilities

• Develop and maintain automation scripts using Linux shell scripting, Python, and other automation tools.
• Ensure seamless deployment and integration between cloud and on-premise environments (AWS).
• Integrate AI and machine learning models into production environments using containerized platforms such as OpenShift.
• Implement and maintain network security protocols to protect AI systems and data pipelines.
• Collaborate with data scientists, DevOps engineers, and architects to translate AI workflows into scalable engineering solutions.
• Monitor, maintain, and optimize system performance, reliability, and scalability.
• Support and maintain CI/CD pipelines for AI model deployment and continuous updates.
• Implement logging, monitoring, and observability frameworks to ensure system stability.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 4+ years of experience in Machine Learning Engineering, MLOps, or AI system integration.
• Strong experience with Bash and Unix/Linux command-line tools.
• Hands-on experience with OpenShift, Docker, and Kubernetes.
• Experience with cloud platforms such as AWS.
• Knowledge of network security and compliance practices related to AI systems.
• Experience with API integrations and microservices architecture.
• Proficiency in Python for automation and machine learning related tasks.
• Experience with workflow orchestration tools such as Ctrl-M.
• Good knowledge of logging and monitoring tools such as Splunk and Geneos.
• Experience with observability frameworks such as Langfuse, Elastic Stack, Grafana, and OpenTelemetry.
• Understanding of Generative AI concepts such as prompt engineering, RAG pipelines, and Agentic AI frameworks.

Job Summary:
We are seeking a skilled ML Ops / AI Integration Engineer to build, integrate, and manage Generative AI and Agentic AI solutions within enterprise environments. The ideal candidate will work closely with data scientists, architects, and DevOps teams to design, deploy, and optimize AI pipelines, ensuring scalable, secure, and reliable AI systems. This role focuses on automation, cloud integration, and production deployment of AI models.

Key Responsibilities

• Develop and maintain automation scripts using Linux shell scripting, Python, and other automation tools.
• Ensure seamless deployment and integration between cloud and on-premise environments (AWS).
• Integrate AI and machine learning models into production environments using containerized platforms such as OpenShift.
• Implement and maintain network security protocols to protect AI systems and data pipelines.
• Collaborate with data scientists, DevOps engineers, and architects to translate AI workflows into scalable engineering solutions.
• Monitor, maintain, and optimize system performance, reliability, and scalability.
• Support and maintain CI/CD pipelines for AI model deployment and continuous updates.
• Implement logging, monitoring, and observability frameworks to ensure system stability.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 4+ years of experience in Machine Learning Engineering, MLOps, or AI system integration.
• Strong experience with Bash and Unix/Linux command-line tools.
• Hands-on experience with OpenShift, Docker, and Kubernetes.
• Experience with cloud platforms such as AWS.
• Knowledge of network security and compliance practices related to AI systems.
• Experience with API integrations and microservices architecture.
• Proficiency in Python for automation and machine learning related tasks.
• Experience with workflow orchestration tools such as Ctrl-M.
• Good knowledge of logging and monitoring tools such as Splunk and Geneos.
• Experience with observability frameworks such as Langfuse, Elastic Stack, Grafana, and OpenTelemetry.
• Understanding of Generative AI concepts such as prompt engineering, RAG pipelines, and Agentic AI frameworks.