ML Engineer

About the Role
We are looking for Traditional Machine Learning, Deep Learning, and AI model development. This is a Data Science-focused role, where approximately 70% of the work involves core AI/ML and Deep Learning, while 30% focuses on LLMs and Generative AI applications.
The ideal candidate should have hands-on experience in building, training, evaluating, deploying, and scaling Machine Learning models in production using any major cloud platform (GCP preferred; AWS or Azure experience is also acceptable). Candidates should possess strong knowledge of statistical modeling, machine learning algorithms, feature engineering, experimentation, and production ML systems.
This role requires someone who can solve real-world business problems using data science, develop scalable AI solutions, and collaborate closely with engineering, product, and business teams.
Key Responsibilities
Traditional Machine Learning & Deep Learning
Design, develop, train, evaluate, and optimize Machine Learning and Deep Learning models for enterprise use cases.
Build predictive, classification, regression, clustering, recommendation, anomaly detection, and optimization models.
Select appropriate ML algorithms based on business requirements and data characteristics.
Perform feature engineering, feature selection, hyperparameter tuning, and model optimization.
Develop scalable AI solutions using Python-based ML frameworks.
Design experiments and validate model performance using appropriate statistical techniques.
Build reusable ML pipelines and automated model training workflows.
Model Deployment & Production AI
Deploy Machine Learning models into production environments.
Build scalable inference pipelines and production-grade ML services.
Monitor model performance, model drift, and data drift.
Optimize models for scalability, latency, and production reliability.
Work with MLOps tools such as Vertex AI, Kubeflow, Airflow, MLflow, Docker, Kubernetes, or similar platforms.
Support CI/CD pipelines for machine learning deployment.
Cloud AI & Data Science
Develop AI solutions using cloud platforms (GCP preferred; AWS or Azure acceptable).
Work with cloud-native ML services and scalable data processing frameworks.
Build and optimize data pipelines supporting ML workflows.
Process structured and unstructured datasets from cloud storage and enterprise systems.
Collaborate with Data Engineering teams for data ingestion, transformation, and feature store development.
AI & Generative AI (Nice to Have)
Develop AI applications leveraging Large Language Models (LLMs).
Work with Retrieval-Augmented Generation (RAG) architectures.
Fine-tune or integrate foundation models where required.
Build AI-powered assistants and intelligent automation solutions.
Evaluate emerging AI technologies and recommend their practical application.
Required Skills & Experience
Core AI / Machine Learning (Must Have)
Strong experience in Traditional Machine Learning.
Hands-on experience with:
Regression
Classification
Clustering
Recommendation Systems
Time Series Forecasting
Anomaly Detection
Optimization Algorithms
Experience in Deep Learning using TensorFlow or PyTorch.
Strong understanding of supervised and unsupervised learning.
Experience designing end-to-end ML solutions.
Programming
Strong hands-on experience in:
Python
NumPy
Pandas
Scikit-learn
SciPy
TensorFlow and/or PyTorch
SQL
Machine Learning Deployment
Experience with:
ML Model Deployment
Production AI Systems
Model Monitoring
Model Drift Detection
Feature Stores
CI/CD for ML
MLOps Practices
Hands-on experience with tools such as:
Vertex AI
Kubeflow
MLflow
Airflow
Docker
Kubernetes
PySpark
Cloud Platforms
Experience working with at least one cloud platform:
Google Cloud Platform (Preferred)
AWS
Microsoft Azure
Knowledge of cloud storage, managed ML services, and scalable AI infrastructure is expected.
Generative AI (Good to Have)
Exposure to:
Large Language Models (LLMs)
RAG
Prompt Engineering
AI Agents
LangChain
Vector Databases
Fine-tuning Foundation Models
LLM experience is considered an advantage but is not the primary focus of this role.
Soft Skills
Strong analytical and problem-solving abilities.
Ability to translate business problems into scalable AI solutions.
Excellent communication and stakeholder management skills.
Experience collaborating with cross-functional engineering and product teams.
Ability to explain technical concepts to both technical and non-technical audiences.
Strong ownership mindset and ability to work independently.
Qualifications
Bachelor's, Master's, or Ph.D. in Computer Science, Data Science, Artificial Intelligence, Mathematics, Statistics, Engineering, Information Science, Physics, Economics, or related quantitative disciplines.
4–8+ years of hands-on experience in Data Science, Machine Learning, or AI.
Proven experience building and deploying production-grade Machine Learning solutions.
Ideal Candidate Profile
The ideal candidate should have:
70% expertise in Traditional Machine Learning and Deep Learning
20% experience in production deployment, MLOps, and cloud-based AI
10% exposure to LLMs, Generative AI, or Agentic AI
This role is intended for candidates with a strong Data Science foundation, rather than those whose experience is primarily focused on prompt engineering or LLM application development. The emphasis is on solving business problems using traditional ML, statistical modeling, deep learning, and production AI systems, with Generative AI serving as an additional capability rather than the core responsibility