Senior Data Scientist (ML Engineer)

Tata Consultancy Services

Johannesburg

On-site

ZAR 1,200,000 - 1,800,000

Full time

2 days ago

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Job summary

Tata Consultancy Services (MEA) is hiring a Senior Data Scientist (ML Engineer) in Johannesburg to productionise and monitor ML models on Databricks. You will deploy AI agents, GenAI apps, and RAG solutions, building reusable ML pipelines with CI/CD and governance.

You will deploy and manage models on AKS, develop APIs, and collaborate with cross-functional teams to ensure scalable, secure AI platforms. You will need strong Python, SQL, API development, and cloud experience, plus hands-on

Qualifications

Strong understanding of MLOps, DevOps and software engineering practices for ML platforms.
Experience building, deploying and supporting ML solutions in production.
Proficiency in Python and experience with SQL and API development.
Experience with Databricks, MLflow, Model Serving and cloud-native AI/ML platforms.
Hands-on experience with Kubernetes, Docker and containerised deployment.
Experience deploying ML on AKS and cloud platforms.
Knowledge of CI/CD pipelines and infrastructure automation.
Experience with distributed computing like Spark.

Responsibilities

Productionise, deploy and monitor ML models and data pipelines on Databricks.
Build, deploy and support AI Agents, GenAI apps and RAG solutions on Databricks.
Develop reusable ML pipelines with CI/CD, tests, monitoring and governance.
Deploy, optimise and manage AI/ML models on AKS.
Create APIs and microservices on AKS to expose ML capabilities.
Implement containerised solutions with Docker and Kubernetes for scalable deployments.
Monitor model performance, drift and reliability in production.
Collaborate with Data Scientists to productionise prototypes into business-ready solutions.
Ensure compliance with enterprise standards across platform teams.
Troubleshoot production issues related to models, pipelines and APIs.
Aim for performance, scalability and cost efficiency in AI/ML solutions.
Contribute to engineering standards and reusable frameworks.
Mentor junior engineers and promote knowledge sharing.

Skills

Databricks Workflows

Model Serving

MLflow

REST APIs

Docker

Kubernetes

CI/CD

MLOps

Machine Learning

Generative AI

LLM deployment

Cloud engineering

Monitoring

Education

Masters or Doctorate advantageous

Computer Science or Engineering background

Econometrics or Mathematical Statistics background

Tools

Databricks

Azure Kubernetes Service (AKS)

Docker

Kubernetes

Job description

Job Title – Senior Data Scientist (ML Engineer)

Company – TCS (MEA)

Location – Johannesburg, South Africa

Job type – Full time

About Us:

Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization that has been partnering with many of the world’s largest businesses in their transformation journeys for over 50 years. TCS offers a consulting-led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions. This is delivered through its unique Location Independent Agile™ delivery model, recognized as a benchmark of excellence in software development.

A part of the Tata group, India's largest multinational business group, TCS has over 616,171 of the world’s best-trained consultants with 157 nationalities in 53 countries. For more information, visit www.tcs.com and follow TCS news at @TCS_News.

Job Description:

Key Responsibilities:

Productionise, deploy and monitor machine learning models and data science pipelines on Databricks.
Build, deploy and support AI Agents, GenAI applications and RAG solutions on Databricks.
Develop and maintain reusable ML pipelines using MLOps principles, including CI/CD, automated testing, monitoring and governance.
Deploy, optimise and manage open source AI and machine learning models on Azure Kubernetes Service (AKS).
Design, develop and support custom APIs and microservices on AKS to expose AI and machine learning capabilities to business applications.
Implement containerised solutions using Docker and Kubernetes to ensure scalable, secure and resilient deployments.
Monitor model performance, drift, reliability and operational health in production environments.
Partner with Data Scientists to productionise prototypes and enable business-ready solutions.
Collaborate with platform, security, cloud and infrastructure teams to ensure compliance with enterprise standards.
Troubleshoot and resolve production issues related to models, pipelines, APIs and AI applications.
Optimise AI and ML solutions for performance, scalability, cost and reliability.
Contribute to engineering standards, reusable frameworks and best practices across the AI and ML ecosystem.
Mentor junior engineers and promote knowledge sharing across the team.
Stay current with advancements in AI, GenAI, MLOps, Databricks, Kubernetes and cloud technologies.
Production-ready ML models and pipelines running on Databricks.
AI Agents and business applications deployed on Databricks.
Open source LLMs and AI services deployed on AKS.
Secure and scalable APIs exposing AI capabilities to consuming systems.
Automated deployment, monitoring and governance processes.
Reliable, scalable and compliant AI platforms supporting business outcomes.

Key Skills:

Databricks Workflows, Model Serving, MLflow and Mosaic AI
Python, SQL and REST APIs
Docker and Kubernetes
CI/CD and MLOps practices
Machine Learning and Generative AI
LLM deployment and optimisation
Cloud engineering and infrastructure automation
Monitoring, observability and troubleshooting

Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuary Science. Masters or Doctorate will be an added advantage.

Preferred Certifications:

Microsoft Azure certifications (AZ-104, AZ-305, AI-102 or equivalent)
Databricks certifications (Data Engineer, Machine Learning Engineer, Generative AI Engineer)
Kubernetes and containerisation certifications (CKA, CKAD or equivalent)
DevOps, MLOps or Platform Engineering certifications
AWS or Google Cloud certifications will be advantageous
Machine Learning, Artificial Intelligence or Data Science certifications from recognised providers such as Microsoft, Databricks, SAS, Coursera or DeepLearning.AI will be an added advantage

Technical / Professional Knowledge:

Strong understanding of MLOps, DevOps and software engineering practices for machine learning platforms.
Experience building, deploying and supporting machine learning solutions in production environments.
Proficiency in Python and experience with SQL and API development.
Experience with Databricks, MLflow, Model Serving and cloud-native AI/ML platforms.
Hands-on experience with Kubernetes, Docker and containerised application deployment.
Experience deploying and supporting machine learning and Generative AI solutions on Azure Kubernetes Service (AKS).
Knowledge of CI/CD pipelines, infrastructure automation and platform monitoring.
Experience with distributed computing technologies such as Spark and large-scale data processing frameworks.
Understanding of machine learning, large language models (LLMs), retrieval-augmented generation (RAG) and AI agents.
Ability to productionise data science solutions and collaborate effectively with Data Scientists.
Experience delivering end-to-end AI and machine learning use cases from development to production.
Ability to translate technical concepts into business outcomes and communicate effectively with stakeholders.
Strong written and verbal communication skills with the ability to work across cross-functional teams.
Self-driven, adaptable and capable of thriving in a fast-paced, technology-driven environment.