Job description

Job Summary

you will bridge the gap between complex data engineering and cutting-edge artificial intelligence. You will architect, build, and scale robust data pipelines while leading the integration of Machine Learning (ML) and Large Language Model (LLM) workflows into our core products.

Key Responsibilities

1. Architecture & Core Engineering

Data & AI Infrastructure: Design, implement, and maintain scalable, fault-tolerant data pipelines (ETL/ELT) and MLOps frameworks to support real-time and batch processing.

AI Integration: Architect and deploy production-grade ML models, Generative AI applications, and Retrieval-Augmented Generation (RAG) systems.

Data Modeling: Establish best practices for data warehousing, lakehouses, and vector databases to support both analytical and operational AI needs.

Performance Tuning: Optimize distributed computing workloads and model inference costs for high throughput and low latency.

2. Leadership & Strategy

Team Mentorship: Guide, code-review, and mentor a team of data and ML engineers, fostering a culture of technical excellence and continuous learning.

AI Roadmap: Partner with leadership to define the company’s AI capabilities, evaluating new frameworks, tools, and SaaS providers.

Governance & Compliance: Define data governance, security policies, and ethical AI guidelines (e.g., data privacy, bias mitigation, model drift tracking).

Skill Requirements

Technical Requirements

Programming: Mastery of Python and solid proficiency in SQL, Scala, or Java.

Data Engineering: Deep experience with modern data stack tools like Spark, Databricks, Snowflake, dbt, and orchestrators like Apache Airflow.

AI & Machine Learning: Practical experience deploying frameworks like PyTorch or TensorFlow, alongside GenAI tools like LangChain, LlamaIndex, and vector databases (e.g., Pinecone, Milvus, or Chroma).

Cloud & DevOps: Strong hands-on experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker, Kubernetes), specifically tailored for MLOps.

Other Requirements

Technical Requirements

Programming: Mastery of Python and solid proficiency in SQL, Scala, or Java.

Data Engineering: Deep experience with modern data stack tools like Spark, Databricks, Snowflake, dbt, and orchestrators like Apache Airflow.

AI & Machine Learning: Practical experience deploying frameworks like PyTorch or TensorFlow, alongside GenAI tools like LangChain, LlamaIndex, and vector databases (e.g., Pinecone, Milvus, or Chroma).

Cloud & DevOps: Strong hands-on experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker, Kubernetes), specifically tailored for MLOps.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Job Summary

Key Responsibilities

1. Architecture & Core Engineering

Data & AI Infrastructure: Design, implement, and maintain scalable, fault-tolerant data pipelines (ETL/ELT) and MLOps frameworks to support real-time and batch processing.

AI Integration: Architect and deploy production-grade ML models, Generative AI applications, and Retrieval-Augmented Generation (RAG) systems.

Data Modeling: Establish best practices for data warehousing, lakehouses, and vector databases to support both analytical and operational AI needs.

Performance Tuning: Optimize distributed computing workloads and model inference costs for high throughput and low latency.

2. Leadership & Strategy

Team Mentorship: Guide, code-review, and mentor a team of data and ML engineers, fostering a culture of technical excellence and continuous learning.

AI Roadmap: Partner with leadership to define the company’s AI capabilities, evaluating new frameworks, tools, and SaaS providers.

Governance & Compliance: Define data governance, security policies, and ethical AI guidelines (e.g., data privacy, bias mitigation, model drift tracking).

Skill Requirements

Technical Requirements

Programming: Mastery of Python and solid proficiency in SQL, Scala, or Java.

Data Engineering: Deep experience with modern data stack tools like Spark, Databricks, Snowflake, dbt, and orchestrators like Apache Airflow.

AI & Machine Learning: Practical experience deploying frameworks like PyTorch or TensorFlow, alongside GenAI tools like LangChain, LlamaIndex, and vector databases (e.g., Pinecone, Milvus, or Chroma).

Cloud & DevOps: Strong hands-on experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker, Kubernetes), specifically tailored for MLOps.

Other Requirements

Technical Requirements

Programming: Mastery of Python and solid proficiency in SQL, Scala, or Java.

Data Engineering: Deep experience with modern data stack tools like Spark, Databricks, Snowflake, dbt, and orchestrators like Apache Airflow.

AI & Machine Learning: Practical experience deploying frameworks like PyTorch or TensorFlow, alongside GenAI tools like LangChain, LlamaIndex, and vector databases (e.g., Pinecone, Milvus, or Chroma).

Cloud & DevOps: Strong hands-on experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker, Kubernetes), specifically tailored for MLOps.