Job ID: JOB_ID_4495
About the Role:
We are seeking a seasoned AI/ML & MLOps Engineer to spearhead the development, deployment, and scaling of our advanced machine learning initiatives. This pivotal role involves bridging the gap between data science and production engineering, ensuring our diverse range of models, from traditional predictive analytics to cutting-edge Generative AI, are robust, scalable, and performant. The ideal candidate is not just a model builder but an architect of automated ‘foundries’ that ensure continuous operation and efficiency.
Key Responsibilities:
- Model Development: Design, train, and optimize machine learning models utilizing frameworks such as PyTorch or TensorFlow.
- GenAI Implementation: Lead the integration of Large Language Models (LLMs), including fine-tuning, advanced prompt engineering, and building sophisticated Retrieval-Augmented Generation (RAG) pipelines.
- Infrastructure & Orchestration: Architect, build, and maintain end-to-end ML pipelines, implementing CI/CD practices specifically for ML workflows using tools like Docker, Kubernetes, MLflow, or Kubeflow.
- Cloud Deployment: Deploy and manage production workloads efficiently on major cloud platforms (AWS, GCP, Azure), with a strong emphasis on cost-effectiveness and low-latency performance.
- Monitoring & Governance: Implement comprehensive monitoring systems to track model drift, data quality, and performance metrics, ensuring 24/7 reliability and operational excellence.
- Collaboration: Work closely with Data Scientists to effectively productize research findings and collaborate with DevOps teams to ensure alignment with enterprise security policies and infrastructure standards.
Technical Requirements:
- Experience: A minimum of 4+ years of hands-on experience in Machine Learning Engineering or MLOps roles.
- Core Stack: Expert-level proficiency in Python programming and standard ML libraries (e.g., Scikit-learn, Pandas, NumPy).
- Deep Learning: Strong, demonstrable experience with deep learning architectures such as Transformers, CNNs, or RNNs.
- DevOps for ML: Mastery of containerization technologies (Docker) and orchestration platforms (Kubernetes). Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation is considered a significant advantage.
- GenAI Tools: Familiarity and practical experience with GenAI tools and frameworks like LangChain, LlamaIndex, and Vector Databases (e.g., Pinecone, Milvus, Weaviate).
Education:
- Bachelor of Science (B.S.) or Master of Science (M.S.) degree in Computer Science, Mathematics, or a closely related quantitative field.
Compensation & Location
Salary: $100,000 – $150,000 per year (Estimated)
Location: Remote
Recruiter / Company – Contact Information
Email: nisha@quantumworldit.com
Recruiter Notice:
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