Data Engineer / AI & ML Engineer

About Company,

Droisys is an innovation technology company focused on helping companies accelerate their digital initiatives from strategy and planning through execution. We leverage deep technical expertise, Agile methodologies, and data-driven intelligence to modernize systems of engagement and simplify human/tech interaction.

Amazing things happen when we work in environments where everyone feels a true sense of belonging and when candidates have the requisite skills and opportunities to succeed. At Droisys, we invest in our talent and support career growth, and we are always on the lookout for amazing talent who can contribute to our growth by delivering top results for our clients. Join us to challenge yourself and accomplish work that matters.

We’re hiring Data Engineer / AI & ML Engineer in Las Vegas, NV.

Position Overview

We are seeking a highly skilled Data Engineer / AI & ML Engineer to design, build, and support enterprise-grade AI systems across large-scale data platforms, computer vision pipelines, and LLM-driven applications.

This role operates within a structured enterprise environment and requires experience building secure, scalable, production-ready systems aligned with governance, compliance, and architectural standards.

Core Responsibilities

1. Enterprise AI & LLM Systems

• Design and implement LLM-powered enterprise applications and RAG pipelines
• Develop secure backend services using Python and FastAPI
• Integrate enterprise APIs, data connectors, and secure data sources
• Support model deployment, monitoring, logging, and SLA-driven performance management
• Implement prompt governance, evaluation pipelines, and hallucination mitigation strategies
• Collaborate with security and architecture teams to align with AI governance frameworks

2. Computer Vision & Machine Learning

• Build and maintain object detection and CV pipelines (YOLO or similar)
• Develop scalable training and inference architectures
• Optimize models for enterprise SLAs (latency, throughput, reliability)
• Support MLOps processes including retraining, versioning, rollback, and traceability
• Ensure model observability and production monitoring

3. Data Engineering & Platform Infrastructure

• Maintain structured and unstructured data ingestion pipelines
• Work with Kafka, Airflow, or equivalent orchestration frameworks
• Build automated retraining and inference pipelines
• Manage cloud-native storage (AWS S3, data lakes, metadata systems)
• Containerize services using Docker and deploy within orchestrated environments
• Implement logging, auditability, and traceability for compliance purposes

4. Frontend & Enterprise Tooling

• Develop internal dashboards and operational monitoring tools
• Build secure Chrome extensions or lightweight web applications
• Integrate frontend systems with authenticated backend services
• Ensure accessibility, usability, and enterprise UI standards

Required qualification :

• Experience in Data Engineering, ML Engineering, or related field
• Strong Python engineering skills in production environments
• Experience deploying ML models.
• Backend API development experience (FastAPI or Flask)
• Working knowledge of JavaScript or TypeScript
• Experience with AWS or equivalent cloud platforms
• Understanding of distributed systems and scalable architecture

Preferred Qualifications

• Experience with LLMs, RAG pipelines, or agent orchestration frameworks
• Experience with computer vision models (YOLO or similar)
• Experience with Kafka, Airflow, or streaming systems
• Familiarity with React or enterprise frontend frameworks
• Experience in regulated or compliance-sensitive environments

Educationa qualification :

• Bachelor’s degree or higher in a relevant field.

Enterprise AI Engineering Competencies

AI-Assisted Development (“Vibe Coding” with Governance)

• Experience using AI-assisted coding tools (Copilot, Cursor, ChatGPT, etc.) within enterprise guardrails
• Ability to accelerate development while adhering to coding standards
• Familiarity with AI code review workflows and prompt governance
• Understanding of IP compliance and secure AI usage policies

MLOps & Platform Engineering

• CI/CD pipelines for ML systems
• Model versioning and experiment tracking
• Performance validation frameworks
• DevSecOps integration
• Infrastructure-as-Code awareness

Representative Technology Stack

Python, PyTorch, Transformers, YOLO, FastAPI, Docker, LangChain/LangGraph (or similar), Kafka, Airflow, AWS, JavaScript, TypeScript, React, Vector Databases, CI/CD pipelines, Observability frameworks, AI-assisted development tools.

Droisys is an equal opportunity employer. We do not discriminate based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. Droisys believes in diversity, inclusion, and belonging, and we are committed to fostering a diverse work environment