Particle41

AI Engineer

• Lead end-to-end development of AI/ML models: from data ingestion & preprocessing to model training, evaluation, deployment and monitoring.
• Build generative-AI solutions (RAG, Agentic Workflows, MCP Servers, Conversation AI Agents) aligned with business goals.
• Work closely with data engineering teams to build/maintain data pipelines, feature stores and orchestration frameworks.
• Develop and integrate AI models into production systems (APIs, microservices, cloud deployments).
• Optimize models/solutions for performance, scalability, robustness and cost-efficiency.
• Monitor models/ solutions’ performance in production (drift, bias, fairness, reliability) and implement remediation.
• Document model design, experiments, data provenance and solution rationale.
• Stay abreast of latest AI/ML research, frameworks, tools (e.g. LangChain, LangGraph, MCP Clients/Servers, Agents SDKs, LiveKit etc.) and propose innovative ideas.
• Work directly with clients to understand problem statements, translate business requirements into AI solutions, and clearly communicate results.

Data Engineer

• Design, develop, and maintain scalable ETL/ELT pipelines to process large volumes of data from diverse sources.
• Build and optimize data storage solutions — data lakes and data warehouses — for efficient retrieval and processing.
• Integrate structured and unstructured data from internal and external systems into a unified view for analysis.
• Ensure data accuracy, consistency, and completeness through validation, cleansing, and transformation.
• Maintain clear documentation for data processes, tools, and systems.
• Build and maintain Tableau dashboards and reports that translate complex datasets into clear, decision-ready visuals.
• Design data models and extracts optimized for Tableau performance, including live connections and published data sources.
• Apply data visualization best practices — chart selection, layout, color, and interactivity — to produce client-ready output.
• Partner with stakeholders to understand reporting needs and translate them into visual solutions.
• Support ad hoc analysis using Tableau, Python-based charting (matplotlib, seaborn, plotly), or similar tools.
• Support AI/ML workflows by building and maintaining the data pipelines that feed model training, inference, and evaluation.
• Assist with data preparation for LLM and machine learning projects, including feature engineering, tokenization pipelines, and vector store integration.
• Help teams adopt AI-assisted data tooling — copilots, intelligent search, automated reporting — by ensuring clean, well-structured data is available upstream.
• Contribute to prompt engineering and evaluation frameworks where data context is a key input.
• Work with product managers and stakeholders to gather requirements and translate them into technical solutions.
• Provide technical input during requirements sessions to align data capabilities with business needs.
• Participate in sprint planning, stand-ups, and sprint reviews.
• Deliver solutions on time and within scope. Adapt when priorities shift.
• Write unit and integration tests to validate pipeline reliability and data accuracy.
• Identify and resolve defects, performance bottlenecks, and data quality issues.
• Stay current with cloud platforms (AWS, Azure, GCP) and emerging data engineering tools.
• Propose solutions to improve performance, security, and scalability.

AI Engineer

Data Engineer, Tableau