We are looking for a hands -on Senior AI/ML Engineer who can own the full lifecycle of machine learning
solutions – from problem definition and data modelling to training, deployment, monitoring, and
continuous improvement.
You should be comfortable working with messy real -world data, designing robust data models &
features, building and training models, and shipping them to production with proper MLOps practices.
You must also be aware of the current AI/ML landscape (LLMs, embeddings, vector search, contemporary
tooling) and know when to use what.
Key Responsibilities:
End -to -End Solution Ownership
- Work with product / domain stakeholders to understand business problems and define ML use
cases
- Translate requirements into data & model design, success metrics, and clear technical plans
- Own the full pipeline: data ingestion â cleaning â feature engineering â model training â
evaluation â deployment â monitoring
Data Modelling & Feature
- Engineering
Design and maintain data models / schemas optimized for analytics and ML training (batch & real
time)
- Perform exploratory data analysis (EDA) and feature engineering to improve signal quality and
model performance
- Work closely with data engineering to ensure reliable, well -documented datasets
Model Training & Evaluation
- Build, train, and tune models for tasks such as: prediction, classification, ranking, recommendations,
anomaly detection, and NLP.
- Use appropriate techniques (traditional ML, deep learning, embeddings, LLMs) based on the
problem
- Define and track offline and online metrics; run A/B tests or controlled experiments where applicable
MLOps & Productionization
- Build reproducible training pipelines (e.g., using MLflow, Airflow, Kubeflow, or similar tools)
- Package and deploy models as APIs / microservices or batch jobs, using containers and cloud
services
- Implement monitoring, alerting, and logging for model performance, data drift, and system health
- Manage model versions, rollouts, and rollback strategies
AI/ML Architecture & Best Practices
- Evaluate and integrate modern AI tools: vector databases, embedding models, LLM APIs, RAG
architectures, etc.
Ensure solutions follow security, privacy, and compliance best practices (e.g., PII handling, access
control)
- Write clear documentation for data flows, models, and services
- Mentor junior engineers/data scientists and contribute to engineering standards and guidelines
Must -Have Skills & Experience
Core Technical Skills
- (6+ Years)
Python Programming: Strong expertise in ML libraries (pandas, numpy, scikit -learn, PyTorch,
TensorFlow)
- SQL & Databases: Solid SQL skills and hands -on experience with relational and NoSQL data stores
- Production ML: Demonstrated experience shipping end -to -end ML projects to production (not just
notebooks / POCs)
- ML Fundamentals: Deep understanding of supervised/unsupervised learning, evaluation metrics,
overfitting, bias/variance, data leakage
- Model versioning and packaging (Docker, virtualenv, Conda)
CI/CD pipelines for ML services
- Infrastructure as Code and containerization best practices
Cloud & Architecture
- Proficiency with at least one major cloud platform:
AWS: S3, EC2, SageMaker, Lambda, RDS, DynamoDB
GCP: Cloud Storage, Compute Engine, Vertex AI, Firestore
- Azure: Blob Storage, VMs, Azure ML, Cosmos DB
API design (REST/GraphQL) and microservice architecture integration
- Understanding of scalability, latency, and cost optimization