Forward Deployment Engineer | Remote

We are seeking an experienced AI/ML & Forward Deployment Engineer to design, develop, and deploy production-grade AI and Machine Learning solutions. This role is ideal for professionals with expertise in Machine Learning, Generative AI, MLOps, and cloud-native application development who enjoy working directly with stakeholders to solve complex business problems. Primary Skills Python Machine Learning Deep Learning Generative AI (GenAI) Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) LangChain / LlamaIndex MLOps / LLMOps Docker Kubernetes CI/CD REST APIs / gRPC AWS / Azure / GCP Git Data Engineering Key Responsibilities Collaborate with business stakeholders and customers to identify, define, and implement AI/ML use cases. Design, develop, and deploy production-ready Machine Learning and Generative AI solutions. Build and optimize RAG pipelines, embeddings, vector search, prompt engineering, and LLM-based applications. Develop and maintain scalable ML models for classification, regression, forecasting, anomaly detection, and NLP. Implement MLOps and LLMOps practices, including model versioning, deployment, monitoring, governance, and automated retraining. Deploy AI services using Docker, Kubernetes, and CI/CD pipelines. Develop secure integration services using REST APIs and gRPC. Collaborate with Data Engineers to build reliable and scalable data pipelines. Monitor model performance, detect drift, troubleshoot production issues, and optimize system performance. Mentor engineering teams and contribute to architecture reviews, technical documentation, and reusable engineering frameworks. Required Qualifications

8+ years of software engineering or AI/ML engineering experience. Strong proficiency in Python programming. Hands-on experience with Machine Learning and Deep Learning frameworks. Experience building Generative AI and Large Language Model (LLM) applications. Practical experience with Retrieval-Augmented Generation (RAG) architectures. Knowledge of LangChain, LlamaIndex, vector databases, and prompt engineering. Experience with Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP). Experience implementing MLOps/LLMOps pipelines and CI/CD. Strong understanding of REST APIs, microservices, and distributed systems. Excellent analytical, communication, and stakeholder management skills. Preferred Qualifications Experience with model monitoring, observability, and governance. Familiarity with experimentation frameworks, A/B testing, and evaluation metrics. Experience working in customer-facing or consulting environments. Knowledge of secure AI deployments, RBAC, encryption, and compliance best practices. If you are passionate about building scalable AI solutions and delivering business impact through modern Machine Learning and Generative AI technologies, we encourage you to apply.

Reflow

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• 8 days ago +

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