Lead Machine Learning Engineer

This role is for one of the Weekday's clients

Min Experience: 5 years

Location: Hyderabad

JobType: full-time

We are seeking an experienced Lead Machine Learning Engineer to lead the design, development, deployment, and operation of enterprise-grade cybertech AI systems. This role combines technical leadership, hands-on model development, and cross-functional system integration.

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 take ownership of the machine learning architecture for threat detection platforms.
- Establish standards for model development, deployment patterns, and quality benchmarks.
- Make well-informed choices regarding model selection, trade-offs, scalability, and cost considerations.
- Conduct code and model evaluations to guarantee enterprise-level reliability and maintainability.
- Mentor machine learning engineers and foster capability development within the team.

2. Model Development & Lifecycle Management

- Design, develop, and deploy a variety of production machine learning models, including:
- Phishing detection
- SMS scam detection
- Call fraud detection
- Keyword and behavioral anomaly detection
- Oversee the complete machine learning lifecycle:
- Data ingestion and preprocessing
- Feature engineering and training
- Model evaluation and validation
- Deployment and monitoring
- Utilize advanced techniques, such as ensemble learning, deep learning, and transfer learning.
- Optimize models for accuracy, latency, scalability, and operational effectiveness.
- Manage model drift, retraining pipelines, and version control processes.

3. ML Infrastructure & Deployment

- Build and maintain production-quality machine learning pipelines.
- Develop and deploy model serving APIs using FastAPI or Flask.
- Containerize applications with Docker and Kubernetes.
- Implement CI/CD pipelines for machine learning workflows.
- Collaborate with backend, platform, and cloud infrastructure teams to ensure seamless system integration.

4. Monitoring, Observability & Explainability

- Design and execute model monitoring, performance tracking, and alerting mechanisms.
- Utilize monitoring tools such as Prometheus and Grafana.
- Employ logging and observability solutions using the ELK Stack or Splunk.
- Ensure model explainability and transparency utilizing SHAP and LIME.
- Facilitate audits, reviews, and operational diagnostics.

5. Data Engineering & Workflow Orchestration

- Design and manage data pipelines and workflows.
- Use Airflow, Prefect, or Dagster for workflow orchestration.
- Conduct large-scale data processing with 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

- Hands-on experience with AWS, GCP, or Azure (SageMaker, Vertex AI, or Azure ML).
- Proficiency in Docker and Kubernetes.
- REST API development experience using FastAPI or Flask.
- Familiarity with CI/CD tools: GitHub Actions or GitLab CI.
- Experience with databases: PostgreSQL and MongoDB.

ML Ops & Experimentation

- Familiar with MLflow and/or Weights & Biases.
- Knowledge of Git and DVC for model and data versioning.
- Experience with Jupyter Notebook / JupyterHub.
- Knowledge of Pytest for testing and validation.
- Familiarity with monitoring & observability tools:
- Prometheus, Grafana
- DataDog, New Relic, or similar APM tools.
- ELK Stack or Splunk.

Professional Qualifications

- 5–7+ years of experience in Machine Learning / Data Science.
- Demonstrated success in deploying machine learning models in production.
- Experience in enterprise or large-scale systems.
- Previous experience in leading or mentoring machine learning engineers.
- Strong grasp of software engineering best practices.

Skills

Machine Learning

ML

Product Management

AI