Senior Data Scientist

Experience: 4 to 7 years

Location: Hyderabad, India

Skills: MLOps, GCP, Vertex AI, Cloud Build, Cloud Run Functions, Kubernetes Engine, CI/CD, Terraform, Python, Bash, Open Telemetry, TensorFlow, PyTorch, scikit-learn, Git, Vertex AI Pipelines, Kubeflow, MLFlow, Jenkins, GitLab CI/CD, GitHub Actions, Docker, Prompt Engineering, ReAct, CoT, Few Shot, RAG, LLM Fine-Tuning, BLUE, ROUGE

Job Summary
To lead advanced data science initiatives, develop predictive models, and drive data-driven decision-making by extracting meaningful insights from complex datasets, enabling business growth and innovation.

Responsibilities:
• Build and maintain MLOps infrastructure on GCP:
○ Design and implement robust and scalable MLOps pipelines using GCP services such as Vertex AI, Cloud Build, Cloud Run Functions, Kubernetes Engine, and more.
• Automate ML workflows:
○ Automate model training, testing, deployment, and monitoring processes using CI/CD pipelines and infrastructure-as-code tools like Terraform.
• Ensure model performance and reliability:
○ Monitor model performance metrics, identify and troubleshoot issues, and implement solutions like hyperparameter tuning to ensure accuracy and high availability.
• Optimize model inference:
○ Optimize model inference performance for low latency and high throughput using techniques like model quantization and acceleration.
• Collaborate with data scientists:
○ Work closely with data scientists to understand model requirements, provide MLOps expertise, and ensure seamless integration of models into production systems.
• Documentation:




○ Document all implemented solutions of every MLOps component designed and developed for the given use case, including Data preparation.

Qualifications (Mandatory):
• Experience:
○ 4-7 years of experience in Machine Learning Operations or AI Engineer (preferably with Google Cloud Platform or other similar Cloud platforms, like AWS and Azure).
• GCP Knowledge:
○ Basic (or in-depth hands-on) understanding of Google Cloud Platform services and architecture.
○ Mandatory Hands-on working familiarity with services like Vertex AI, Compute Engine, Cloud Storage, Cloud Functions, Cloud Run, Pub-Sub, BigQuery (and/or any other associated services’ offerings on GCP).
• MLOps Tools: Hands-on experience with MLOps tools and technologies, such as:
○ MLOps Frameworks: Vertex AI Pipelines, Kubeflow, MLFlow.
○ CI/CD: Jenkins, GitLab CI/CD, GitHub Actions, or Cloud Build (GCP)
○ Containerization: Docker
○ [Optional] Infrastructure-as-code: Terraform
○ Monitoring: Cloud Monitoring
• Programming:
○ Proficiency in Python and experience with scripting languages like Bash.
○ Overall idea (or hands-on experience) of Open Telemetry (OTEL) (Good to have, not mandatory)
• ML Frameworks:
○ Working (hands-on) Experience with popular machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.
• Version Control: Experience with Git and version control best practices.




• Problem-solving: Strong analytical and problem-solving skills.
• Communication: Excellent communication and collaboration skills.
• [Additional] LLM skills (For LLMOps projects):
○ Prompt Engineering: Experience with using Prompt Engineering techniques (ReAct, CoT, Few Shot).
○ RAG: Experience in working with vector databases and developing RAG based solutions.
○ LLM Fine-Tuning: Experience in fine-tuning pre-trained LLM on specific tasks.
○ Evaluation: Well versed with LLM evaluation techniques and metrics such as BLUE, ROUGE, etc., for assessing model quality.

Certifications & Trainings:
• Qualified Machine Learning Engineer (https://cloud.google.com/learn/certification/guides/machine-learning-engineer)
• Cloud Skills Boost training/labs on Machine

Key Responsibilities
1. Develop and deploy machine learning models, predictive analytics, and AI solutions to solve business challenges.
2. Design and optimize algorithms for data processing, feature engineering, and pattern recognition.
3. Lead data exploration, mining, and visualization to uncover trends and actionable insights.
4. Collaborate with cross-functional teams to integrate data science solutions into business strategies.
5. Drive innovation by leveraging advanced statistical techniques, deep learning, and big data technologies.
6. Ensure data integrity, governance, and best practices in model development and deployment.
7. Mentor junior data scientists and promote a data-driven culture within the organization.
8. Stay ahead of industry trends and emerging technologies to enhance analytical capabilities.

📌 Senior Data Scientist (Secunderabad)
🏢 HCL Technologies
📍 Secunderabad