Accolite

Data AI Engineer

Job Description

Job Summary

We are looking for a motivated Data & AI Engineer with 2–4 years of experience who has a strong foundation in data engineering and AI/ML concepts, along with hands-on development experience in n8n for workflow automation and integrations. The ideal candidate should be comfortable working across data pipelines, cloud data platforms, AI-driven use cases, and low-code automation workflows, while collaborating closely with cross-functional teams.

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Key Responsibilities

Data Engineering & Analytics

  • · Design, develop, and maintain end-to-end data pipelines for ingestion, transformation, and storage.
  • · Work with structured and semi-structured data across data lakes, warehouses, and analytics platforms.
  • · Develop and optimize ETL/ELT workflows using SQL, Python, and cloud-native services.
  • · Ensure data quality, reliability, and performance through validation, monitoring, and optimization.
  • · Collaborate with stakeholders to understand data requirements and translate them into scalable solutions.

AI / ML Responsibilities

  • · Support development and deployment of AI/ML models for analytics, automation, and business insights.
  • · Work with data scientists to prepare datasets for training, testing, and inference.
  • · Implement and integrate AI-driven APIs and services (e.g., prediction, classification, recommendation, NLP).
  • · Assist in model evaluation, monitoring, and performance tuning in production environments.
  • · Apply responsible AI practices, including explainability, data privacy, and governance.

n8n Development & Automation

  • · Design, build, and maintain n8n workflows for data orchestration, automation, and system integrations.
  • · Develop custom n8n nodes or extend existing nodes when required.
  • · Integrate n8n with databases, cloud services, REST APIs, messaging platforms, and third-party tools.
  • · Implement error handling, logging, retries, and performance optimizations in workflows.
  • · Collaborate with engineering and business teams to automate manual processes and improve efficiency.

Development & Collaboration

  • · Write clean, maintainable, and well-documented code.
  • · Participate in code reviews, design discussions, and sprint planning.
  • · Troubleshoot and resolve production issues related to data pipelines, AI workflows, and automations.
  • · Follow best practices for version control, CI/CD, security, and scalability.

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Required Skills & Qualifications

Technical Skills

  • · 2–4 years of experience in Data Engineering, AI/ML, or related roles.
  • · Strong proficiency in SQL and Python.
  • · Experience with data pipelines, ETL/ELT frameworks, and data modeling.
  • · Hands-on experience with n8n for workflow automation and integrations.
  • · Familiarity with REST APIs, JSON, webhooks, and event-driven architectures.
  • · Exposure to cloud platforms (AWS / Azure / GCP) and their data services.
  • · Understanding of AI/ML fundamentals (supervised/unsupervised learning, model lifecycle).
  • · Experience working with databases (PostgreSQL, MySQL, SQL Server, or NoSQL).

Tools & Technologies (Good to Have)

  • · Data platforms: Snowflake, BigQuery, Redshift, Synapse, Databricks
  • · AI/ML tools: scikit-learn, TensorFlow, PyTorch, MLflow
  • · Workflow & orchestration: n8n, Airflow, Azure Data Factory
  • · Version control: Git, GitHub/GitLab
  • · Containers & DevOps basics: Docker, CI/CD pipelines

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Soft Skills

  • · Strong analytical and problem-solving skills.
  • · Ability to understand business requirements and translate them into technical solutions.
  • · Good communication skills and ability to work with cross-functional teams.
  • · Eagerness to learn new tools and technologies in the Data & AI space.
  • · Attention to detail and ownership mindset.

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Nice to Have

  • · Experience building AI-powered automations using n8n.
  • · Exposure to LLMs, prompt engineering, or GenAI integrations.
  • · Knowledge of data governance, security, and compliance practices.
  • · Experience in Agile/Scrum environments.


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