AI/ML/GenAI Engineer_Remote_WFH

AI/ML/GenAI Engineer

Key Responsibilities

Core ML Engineering & Infrastructure

• Design and implement robust, scalable data pipelines using modern tools like Apache Spark, Dask, and Databricks to ingest, clean, and transform large datasets.
• Collaborate with Data Scientists to translate research prototypes and experimental models into production-ready, enterprise-grade solutions.
• Implement a comprehensive MLOps lifecycle, including model versioning, automated deployment, real-time monitoring, A/B testing, and intelligent retraining strategies.
• Build and optimize low-latency, high-availability model serving infrastructure that supports both batch and real-time inference at scale.

Advanced AI & Generative AI Development

• Develop and deploy Large Language Models (LLMs) and Generative AI solutions for call center applications, including conversation analysis, automated response generation, and customer sentiment understanding.
• Implement Retrieval-Augmented Generation (RAG) systems using vector databases such as FAISS, Pinecone, and Weaviate for intelligent knowledge retrieval and context-aware responses.
• Design multi-modal AI systems that integrate text, voice, and structured data for comprehensive customer interaction analysis.
• Apply prompt engineering techniques and fine-tuning strategies to optimize LLM performance for domain-specific use cases.

Production Systems & Automation

• Automate end-to-end ML workflows from data collection and feature engineering to model deployment and performance monitoring.
• Build real-time inference systems capable of processing thousands of concurrent requests with sub-second response times.
• Implement advanced monitoring and alerting systems for model performance, data drift detection, and system health.
• Develop CI/CD pipelines specifically designed for ML workflows with automated testing, validation, and rollback capabilities.

Required Skills & Experience

Experience & Education

• 5-8 years of professional experience in Data Science, ML/DL Architecturing, Generative AI engineering, or AI/ML development roles.
• A proven track record of deploying and maintaining production ML systems at scale is required.
• Experience in customer service, telecommunications, or related industries is strongly preferred.
• A Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Mathematics, or a related technical field is required, with an advanced degree preferred for senior-level positions.

Technical Expertise

• Programming & Core Libraries:

  Mastery of the Python Ecosystem, including advanced proficiency with Python 3.9+, Pandas 2.0+, and NumPy 1.24+. You should also have experience with big data processing using PySpark and Dask.

• Machine Learning Frameworks:

  Expert-level experience with deep learning platforms like PyTorch 2.0+ and TensorFlow 2.13+, as well as gradient boosting libraries such as XGBoost, LightGBM, and CatBoost.

• Generative AI & LLMs:

  Production experience with Large Language Model platforms is required. This includes knowledge of the OpenAI ecosystem (GPT-4, Whisper), Google AI Platform (Gemini Pro/Ultra), and Meta AI models (Llama 2/3). You should be proficient with LLM development frameworks like LangChain, LangGraph, and the Hugging Face ecosystem.

• MLOps:

  You must have production MLOps experience with tools like MLflow, Kubeflow, and AWS SageMaker. Containerization and orchestration skills with Docker and Kubernetes are also essential.