Role: Senior MLOps / Machine Learning Engineer (GenAI)
Budget: $2 – $8 USD
Overview
Design, build, deploy, and operate production-grade machine learning and GenAI systems at scale. The role focuses on MLOps, AI infrastructure, data pipelines, and RAG-based GenAI solutions, with strong emphasis on reliability, scalability, and real-world operational impact.
Responsibilities
Design and implement CI/CD pipelines for machine learning and GenAI model deployment.
Containerize and orchestrate ML services using Docker and Kubernetes.
Deploy, monitor, and manage ML models in production environments.
Implement model performance monitoring, drift detection, and model versioning.
Build and maintain robust data pipelines for structured and unstructured data ingestion, transformation, and validation.
Develop, deploy, and operate RAG-based GenAI systems using LLMs and vector databases.
Integrate LLM workflows into production systems with reliability and observability.
Collaborate with data scientists and software engineers to align AI solutions with operational requirements.
Ensure compliance with data governance, privacy, and regulatory constraints.
Support scalable, secure, and fault-tolerant AI platforms in cloud environments.
Required Technical Skills
Machine Learning & GenAI
Production deployment of machine learning models
Experience with LLMs, GenAI, or NLP pipelines
RAG frameworks: LangChain, Haystack, LlamaIndex
ML frameworks: TensorFlow, PyTorch
Embedding models and vector-based retrieval
MLOps & Data Engineering
MLOps tooling: MLflow, Airflow, SageMaker AI
Model lifecycle management (training → deployment → monitoring)
PySpark for large-scale data processing
Data pipeline design for structured and unstructured data
Cloud, DevOps & Infrastructure
AWS (preferred cloud platform)
Docker and Kubernetes
CI/CD pipelines for ML workloads
Infrastructure as Code (e.g., Terraform)
Monitoring and observability for ML services
Vector Databases
FAISS
Weaviate
Pinecone
Programming
Python (primary)
PySpark
Additional / Nice-to-Have Skills
Geospatial data processing
Domain-specific analytics on telemetry-style datasets
Distributed systems experience
Working with globally distributed teams
Agile development practices
Strong written and verbal technical communication
Design, build, deploy, and operate production-grade machine learning and GenAI systems at scale. The role focuses on MLOps, AI infrastructure, data pipelines, and RAG-based GenAI solutions, with strong emphasis on reliability, scalability, and real-world operational impact.
Responsibilities
Design and implement CI/CD pipelines for machine learning and GenAI model deployment.
Containerize and orchestrate ML services using Docker and Kubernetes.
Deploy, monitor, and manage ML models in production environments.
Implement model performance monitoring, drift detection, and model versioning.
Build and maintain robust data pipelines for structured and unstructured data ingestion, transformation, and validation.
Develop, deploy, and operate RAG-based GenAI systems using LLMs and vector databases.
Integrate LLM workflows into production systems with reliability and observability.
Collaborate with data scientists and software engineers to align AI solutions with operational requirements.
Ensure compliance with data governance, privacy, and regulatory constraints.
Support scalable, secure, and fault-tolerant AI platforms in cloud environments.
Required Technical Skills
Machine Learning & GenAI
Production deployment of machine learning models
Experience with LLMs, GenAI, or NLP pipelines
RAG frameworks: LangChain, Haystack, LlamaIndex
ML frameworks: TensorFlow, PyTorch
Embedding models and vector-based retrieval
MLOps & Data Engineering
MLOps tooling: MLflow, Airflow, SageMaker AI
Model lifecycle management (training → deployment → monitoring)
PySpark for large-scale data processing
Data pipeline design for structured and unstructured data
Cloud, DevOps & Infrastructure
AWS (preferred cloud platform)
Docker and Kubernetes
CI/CD pipelines for ML workloads
Infrastructure as Code (e.g., Terraform)
Monitoring and observability for ML services
Vector Databases
FAISS
Weaviate
Pinecone
Programming
Python (primary)
PySpark
Additional / Nice-to-Have Skills
Geospatial data processing
Domain-specific analytics on telemetry-style datasets
Distributed systems experience
Working with globally distributed teams
Agile development practices
Strong written and verbal technical communication