Description
Lead Machine Learning Engineer
Team: Technology, Information and Media
Location: Hyderabad
Commitment: Full-time
Workplace Type: onsite
Salary:
The ideal candidate will own multiple production ML models for threat detection and ensure their performance, reliability, and scalability across environments while mentoring engineers and driving technical excellence.
Key Responsibilities
1. Technical Leadership & Architecture
Design and own the ML architecture for threat detection platforms
Define model development standards, deployment patterns, and quality benchmarks
Make informed decisions on model selection, trade-offs, scalability, and cost
Conduct code and model reviews to ensure enterprise-level reliability and maintainability
Mentor ML engineers and contribute to capability building within the team
2. Model Development & Lifecycle Ownership
Design, develop, and deploy multiple production ML models, including:
Phishing detection
SMS scam detection
Call fraud detection
Keyword and behavioral anomaly detection
Own the end-to-end ML lifecycle:
Data ingestion and preprocessing
Feature engineering and training
Evaluation and validation
Deployment and monitoring
Apply advanced techniques such as ensemble learning, deep learning, and transfer learning
Optimize models for accuracy, latency, scalability, and operational efficiency
Manage model drift, retraining pipelines, and version control
3. ML Infrastructure & Deployment
Build and maintain production-grade ML pipelines
Develop and deploy model serving APIs using FastAPI or Flask
Containerize applications using Docker and Kubernetes
Implement CI/CD pipelines for ML workflows
Collaborate with backend, platform, and cloud infrastructure teams for seamless system integration
4. Monitoring, Observability & Explainability
Design and implement model monitoring, performance tracking, and alerting
Use monitoring tools such as Prometheus and Grafana
Implement logging and observability using ELK Stack or Splunk
Ensure model explainability and transparency using SHAP and LIME
Support audits, reviews, and operational diagnostics
5. Data Engineering & Workflow Orchestration
Design and manage data pipelines and workflows
Use Airflow, Prefect, or Dagster for orchestration
Perform large-scale data processing using SQL and Apache Spark
Integrate real-time and streaming systems using Kafka or RabbitMQ
Required Technical Skills
Machine Learning & Programming
Expert proficiency in Python
Strong experience with:
scikit-learn, XGBoost, LightGBM
TensorFlow and PyTorch
Hugging Face, spaCy, NLTK
Pandas, NumPy, Matplotlib, Seaborn
Optuna or scikit-optimize
Cloud & DevOps
Production experience with AWS, GCP, or Azure
(SageMaker, Vertex AI, or Azure ML)
Docker and Kubernetes
REST API development using FastAPI or Flask
CI/CD tools: GitHub Actions or GitLab CI
Databases: PostgreSQL and MongoDB
ML Ops & Experimentation
MLflow and/or Weights & Biases
Git and DVC for model and data versioning
Jupyter Notebook / JupyterHub
Pytest for testing and validation
Monitoring & Observability
Prometheus, Grafana
DataDog, New Relic, or equivalent APM tools
ELK Stack or Splunk
Professional Qualifications
5–7+ years of experience in Machine Learning / Data Science
Proven track record of deploying ML models into production
Experience working in enterprise or large-scale systems
Prior experience leading or mentoring ML engineers
Strong understanding of software engineering best practices
This role is for one of our clients
Industry: Technology, Information and Internet
Seniority level: Mid-Senior level
Min Experience: 5 years
Location: Hyderabad
JobType: full-time
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