Machine Learning Engineer

Role Overview

We are hiring an AI / Machine Learning Engineer to build
production-grade ML systems
that operate at scale. This role focuses on turning data and models into reliable, high-performance services that directly impact business outcomes.

You will own the
end-to-end ML lifecycle
- from problem framing to deployment, monitoring, and continuous improvement.

What You’ll Own

- Translate business problems into
ML problem statements and measurable objectives
- Design and build
scalable ML pipelines
(batch + real-time)
- Develop and train models for prediction, ranking, and optimization
- Engineer robust
feature pipelines and data transformations
- Deploy models as
low-latency APIs or streaming services
- Implement
continuous training and model retraining pipelines
- Monitor
data drift, model drift, and system performance
- Conduct
experimentation (A/B testing, offline/online validation)
- Optimize systems for
accuracy, latency, and cost
- Ensure reliability with
logging, monitoring, and alerting systems

Core Technical Requirements

- Strong programming in Python with production-level standards
- Deep understanding of
machine learning fundamentals, supervised
& unsupervised learning
- Ensemble methods (Random Forest, XGBoost)
- Solid foundation in
statistics and probability
- Experience with
feature engineering and data pipelines
- Hands-on with ML frameworks (Scikit-learn, TensorFlow, PyTorch)
- Experience deploying models using
REST/gRPC APIs
- Understanding of
system design (scalability, fault tolerance, latency)

Data & Systems Engineering Expectations

- Strong SQL and data modeling skills
- Experience with
large-scale data processing (Spark or equivalent)
- Understanding of
data pipelines (ETL/ELT workflows)
- Familiarity with
streaming systems (Kafka or similar)
- Ability to debug and optimize
data + model pipelines end-to-end

MLOps & Production Readiness

- Experience with
CI/CD for ML systems
- Model versioning, experiment tracking, and reproducibility
- Monitoring pipelines for
data quality and model performance
- Experience with containerization (Docker) and orchestration (Kubernetes)
- Handling
rollback, failure recovery, and deployment strategies

Cloud & Infrastructure

- Experience with at least one cloud platform (AWS / Azure / GCP)
- Understanding of
distributed systems and scalable architecture
- Ability to optimize
compute cost vs performance trade-offs

Good to Have (Strong Differentiators)

- Deep learning (CNNs, transformers)
- Exposure to
Generative AI / LLM systems
- Experience with
recommendation systems or ranking models
- Knowledge of
feature stores and online/offline consistency
- Familiarity with
model explainability and fairness techniques

Qualifications

• Bachelor’s or Master’s in Computer Science, AI, Data Science, or related field
• 3–8+ years of experience building ML systems in production
• Strong fundamentals in math, algorithms, and data structures

How You Will Be Measured

• Model performance (accuracy, precision/recall, business metrics)
• System latency and throughput
• Reliability (uptime, failure rate, recovery time)
• Impact on business KPIs (revenue, cost, efficiency)

What Top Candidates Do Differently

- Think in
systems, not just models
- Balance
accuracy vs latency vs cost
- Build
reusable, scalable ML infrastructure
- Communicate clearly with both engineers and business stakeholders

Typical High-Impact Projects

• Real-time recommendation and ranking systems
• Fraud detection and risk scoring pipelines
• Demand forecasting and supply optimization
• Customer churn prediction and targeting models
• Personalization engines for large-scale platforms

Reality of the Role (No Fluff)

This is not a “train model and done” role.

You are expected to:

• Work with messy, incomplete data
• Debug pipelines in production
• Handle scale and failures
• Deliver measurable business value

Why This Role Matters

You will directly influence:

• Product decisions
• Customer experience
• Revenue and cost optimization