Job Title: Machine Learning Engineer

Location: India

Job Type: Full Time

---

Job Overview:

About the job

Remote Work: Hybrid

Overview:

At Zebra, Machine Learning Engineer we are a community of innovators who come together to create new ways of working to make everyday life better. United by curiosity and care, we develop dynamic solutions that anticipate our customer’s and partner’s needs and solve their challenges.

Being a part of Zebra Nation means being seen, heard, valued, and respected. Drawing from our diverse perspectives, we collaborate to deliver on our purpose. Here you are a part of a team pushing boundaries to redefine the work of tomorrow for organizations, their employees, and those they serv

You have opportunities to learn and lead at a forward-thinking company, defining your path to a fulfilling career while channeling your skills toward causes that you care about – locally and globally. We’ve only begun reimaging the future – for our people, our customers, and the world.

Let’s create tomorrow together.

Machine Learning Engineer

Highly skilled and motivated Data Scientist (LLM Specialist) to join our Machine Learning Engineer AI/ML team. This role is ideal for an individual passionate about Large Language Models (LLMs), workflow automation, and customer-centric AI solutions. You will be responsible for building robust ML pipelines, designing scalable workflows, interfacing with customers, and independently driving research and innovation in the evolving agentic AI space.

Key Responsibilities:

• Machine Learning Engineer LLM Development & Optimization: Train, fine-tune, evaluate, and deploy Large Language Models (LLMs) for various customer-facing applications.
• Pipeline & Workflow Development: Build scalable machine learning workflows and pipelines that facilitate efficient data ingestion, model training, and deployment.
• Model Evaluation & Performance Tuning: Implement best-in-class evaluation metrics to assess model performance, optimize for efficiency, and mitigate biases in LLM applications.
• Customer Engagement: Collaborate closely with customers to understand their needs, design AI-driven solutions, and iterate on models to enhance user experiences.
• Research & Innovation: Stay updated on the latest developments in LLMs, agentic AI, reinforcement learning with human feedback (RLHF), and generative AI applications. Recommend novel approaches to improve AI-based solutions.
• Infrastructure & Deployment: Work with MLOps tools to streamline deployment and serve models efficiently using cloud-based or on-premise architectures, including Google Vertex AI for model training, deployment, and inference.
• Foundational Model Training: Experience working with open-weight foundational models, leveraging pre-trained architectures, fine-tuning on domain-specific datasets, and optimizing models for performance and cost-efficiency.
• Cross-Functional Collaboration: Partner with engineering, product, and design teams to integrate LLM-based solutions into customer products seamlessly.
• Ethical AI Practices: Ensure responsible AI development by addressing concerns related to bias, safety, security, and interpretability in LLMs.
  

Responsibilities:

Education: Bachelor’s/Master’s/Ph.D. in Computer Science, Machine Learning, AI, Data Science, or a related field.

• Experience: experience in ML, NLP, or AI-related roles, with a focus on LLMs and generative AI.
• Programming Skills: Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch
• LLM Expertise: Machine Learning Engineer Hands-on experience in training, fine-tuning, and deploying LLMs
  

(e.g., OpenAI’s GPT, Meta’s LLaMA, Mistral, or other transformer-based architectures).

• Foundational Model Knowledge: Strong understanding of open-weight LLM architectures, including training methodologies, fine-tuning techniques, hyperparameter optimization, and model distillation.
• Data Pipeline Development: Strong understanding of data engineering concepts, feature engineering, and workflow automation using Airflow or Kubeflow.
• Cloud & MLOps: Machine Learning Engineer Experience deploying ML models in cloud environments like AWS, GCP (Google Vertex AI), or Azure using Docker and Kubernetes.
• Model Serving & Optimization: Proficiency in model quantization, pruning, distillation, and knowledge distillation to improve deployment efficiency and scalability.
• Research & Problem-Solving: Ability to conduct independent research, explore novel solutions, and implement state-of-the-art ML techniques.
• Strong Communication Skills: Ability to translate technical concepts into actionable insights for non-technical stakeholders.
• Version Control & Collaboration: Proficiency in Git, CI/CD pipelines, and working in cross-functional teams.
  

Nice-to-Have:

• Experience with Reinforcement Learning (RLHF) for LLMs.
• Knowledge of vector databases and retrieval-augmented generation (RAG) architectures.
• Familiarity with multi-modal AI models (vision-language models, speech-to-text, etc.).
• Understanding of agentic AI frameworks (e.g., AutoGPT, LangChain, LlamaIndex).
• Hands-on experience with Google Vertex AI Pipelines, AutoML, and model monitoring.
  

Machine Learning Engineer are seeking LLM enthusiast with a knack for research, customer interaction, and building impactful AI solutions

Qualifications:

• Bachelor’s degree. Advanced degree–masters or PhD-strongly preferred in Statistics, Mathematics, Data / Computer Science or related discipline
• 2-5 years experience
• Statistics modeling and algorithms
• Machine Learning Experience–including deep learning and neural networks, genetics algorithm etc.
• Working knowledge Big Data–Hadoop, Cassandra,Spark R. Hands-on experience preferred
• Data Mining
• Data Visualization and visualization and analysis tools including R
• Work/Project experience in sensors, IoT, mobile industry highly preferred
• Excellent verbal and written communication
• Comfortable with presenting to senior management and CxO level executives
• Self motivated and self starter with high degree of work ethic
  

To protect candidates from falling victim to online fraudulent activity involving fake job postings and employment offers, please be aware our recruiters will always connect with you via @zebra.com email accounts. Machine Learning Engineer Applications are only accepted through our applicant tracking system and only accept personal identifying information through that system. Our Talent Acquisition team will not ask for you to provide personal identifying information via e-mail or outside of the system. If you are a victim of identity theft contact your local police department.

How to Apply:

Please submit your resume and cover letter through Submit Resume to apply for this position.

Apply Here

Join Our WhatsApp Community: Get Updates

Join Our Telegram Channel: Get Updates 

Follow Us on Instagram: Get Updates

DevOps Job Update

• The Future of Cloud Engineering in 2025: Trends, Skills, and Opportunities
• How to Become a Successful AI Engineer in 2025: The Ultimate Guide
• Software Engineer in 2025: The Shocking Truth About AI’s Impact
• Upgraded 5 LPA Software Engineer Intern (Product) Role at Teal India – Apply Now!
• Cloud Engineer AI Jobs in 2025 – High-Demand Skills & Salary Guide!