Freelance AI / ML Engineer (ETL + MLOps + AWS)

Company Description

ThreatXIntel is a dedicated cybersecurity startup specializing in protecting businesses and organizations from cyber threats. With services such as cloud security, web and mobile security testing, cloud security assessment, and DevSecOps, we provide tailored and cost-effective solutions to meet the specific needs of our clients. As a company, we emphasize a proactive approach by continuously monitoring and assessing vulnerabilities to safeguard digital assets. ThreatXIntel is driven by its mission to deliver exceptional cybersecurity services, empowering clients to focus on their business growth with peace of mind.

Role Description

We are looking for an experienced

Freelance AI / ML Engineer

to design, build, and optimize large-scale

data pipelines ,

ETL workflows , and

MLOps systems

on AWS. The ideal candidate has deep hands-on expertise in Python, cloud-native ETL, production ML pipelines, and end-to-end machine learning engineering.

This role involves close collaboration with data scientists, engineers, and business stakeholders to build scalable, secure, and automated data & ML pipelines.

Key Responsibilities

Data Engineering & ETL Development

Design, develop, and maintain complex ETL pipelines using AWS services (Glue, Lambda, EMR, Step Functions, S3).

Build scalable, high-performance data ingestion and transformation workflows.

Implement data quality checks, schema validation, and automated error handling.

MLOps & Model Pipeline Engineering

Develop end-to-end MLOps pipelines for training, validation, deployment, and monitoring.

Integrate CI / CD workflows for ML using CodePipeline, GitHub Actions, or similar tools.

Set up model versioning, lineage tracking, and experiment management.

Model Deployment & Monitoring

Deploy machine learning models to AWS SageMaker endpoints (real-time, batch).

Build automated monitoring for performance drift, data drift, and model stability.

Investigate alerts and coordinate timely resolution of production issues.

Data Analysis & Feature Engineering

Conduct exploratory data analysis (EDA) to support ML model development.

Develop scalable feature engineering workflows and maintain feature documentation.

Validate model-ready datasets in collaboration with data scientists.

Root Cause Analysis & Data Quality

Track data lineage, diagnose data quality issues, and perform root cause analysis.

Document assumptions, transformation logic, and data contracts.

Required Skills & Experience

8–10 years

in AI Engineering, ML Engineering, or Data Engineering.

7+ years

building scalable AI / ML or data pipelines.

3+ years

building AWS-based ETL pipelines (Glue, EMR, Lambda, Step Functions).

Strong hands-on experience with

Python

and ML libraries (Scikit-learn, Pandas, NumPy, TensorFlow / PyTorch).

Proven experience deploying and managing production models on

AWS SageMaker .

Strong understanding of software engineering best practices : version control, testing, design patterns.

Knowledge of machine learning development lifecycle (MDLC) and MLOps principles.

Experience with CI / CD, Docker, Git, and automated testing for data and model pipelines.

Nice to Have

Experience with big data frameworks (Spark, EMR).

Familiarity with MLflow, SageMaker Feature Store, or other metadata systems.

Experience with Terraform or CloudFormation (IaC).

Exposure to LLMOps or Generative AI pipelines.

Knowledge of real-time streaming tools (Kafka / Kinesis).