Principal Engineer, Data Analytics Engineering

Position: Principal AI Engineer (Data Engineering & GenAI)

We are seeking a Principal AI Engineer with deep expertise in data engineering, Generative AI (GenAI), and AWS cloud platforms to architect and lead scalable AI-driven solutions that address complex business and technology challenges.

Key Responsibilities

• AI Platform Architecture:

  Design and implement scalable AI/ML and GenAI solutions on AWS using modern data engineering frameworks and best practices.

• Data Engineering Leadership:

  Drive the development of robust data pipelines, ETL/ELT frameworks, and data models that support AI/ML and analytics use cases at scale.

• Generative AI Solutions:

  Lead the exploration, prototyping, and deployment of GenAI applications (LLM-based copilots, RAG pipelines, autonomous agents) across enterprise scenarios.

• Technology Strategy:

  Evaluate and adopt emerging technologies to strengthen AI and data engineering efficiency and capabilities.

• Cloud & Infrastructure:

  Define and implement cloud-native architectures using AWS services (S3, Glue, Redshift, EMR, Lambda, SageMaker, Bedrock, EKS).

• Collaboration & Influence:

  Partner with data scientists, product managers, and business stakeholders to translate business needs into scalable technical solutions.

• Best Practices & Governance:

  Establish coding standards, DevOps/MLOps practices, and enforce data governance and security controls for AI workloads.

• Mentorship & Leadership:

  Guide and mentor engineers and data professionals, fostering innovation, technical excellence, and best practices.

Basic Qualifications

• Bachelor s or Master s degree in

  Computer Science, Data Engineering, AI/ML

  , or related field.

• 9+

  years of experience in

  data engineering, AI/ML systems, and cloud computing

  .
• Proven expertise with

  AWS cloud ecosystem

  (S3, Glue, Redshift, EMR, Lambda, SageMaker, Bedrock) and

  Databricks

  .
• Hands-on experience with

  Generative AI models

  (LLMs, diffusion models), frameworks (LangChain, Hugging Face, OpenAI/Anthropic APIs), and

  RAG

  implementations.
• Strong programming skills in

  Python, SQL

  , and at least one compiled language (

  Java

  , Scala, or Go).
• Experience with

  modern data engineering tools and pipelines

  (Airflow, Spark, Kafka, dbt, Snowflake).
• Excellent problem-solving skills with experience in architecture design and stakeholder communication.

Preferred Qualifications

• Experience with

  vector

  and

  graph

  databases (e. g. , Pinecone, Weaviate, pgvector, Neo4j).
• Working knowledge of

  MCP

  (Model Context Protocol) for orchestrating and managing AI agent workflows.
• Familiarity with containerization and orchestration (Docker, Kubernetes, EKS)
• Knowledge of

  MLOps/LLMOps

  practices and

  CI/CD pipelines

  for AI workloads (e. g. , MLflow, Kubeflow, LangSmith).
• Strong understanding of data security, compliance, and governance in cloud and AI systems.