Freelance ai/ml engineer

2 days ago

Alwar, India ThreatXIntel Full time

Company Description Threat XIntel 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 Dev Sec Ops, 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. Threat XIntel 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 Code Pipeline, Git Hub Actions, or similar tools. Set up model versioning, lineage tracking, and experiment management. Model Deployment & Monitoring Deploy machine learning models to AWS Sage Maker 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, Num Py, Tensor Flow/Py Torch). Proven experience deploying and managing production models on AWS Sage Maker . 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, Sage Maker Feature Store, or other metadata systems. Experience with Terraform or Cloud Formation (Ia C). Exposure to LLMOps or Generative AI pipelines. Knowledge of real-time streaming tools (Kafka/Kinesis).

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