Forward Deployed Engineer

OverviewHelp design, build and continuously improve the clients online platform.Research, suggest and implement new technology solutions following best practices/standards.Take responsibility for the resiliency and availability of different products.Be a productive member of the team.ResponsibilitiesLead end-to-end technical deployments of the PHTN.ai platform within enterprise environments.Design and implement robust integration pipelines connecting customer data sources, APIs, and systems of record.Deploy, scale, and monitor machine learning models in production to ensure reliability and performance.Automate deployments using CI/CD pipelines, infrastructure-as-code, and container orchestration technologies such as Docker and Kubernetes.Develop custom extensions, SDKs, and APIs to tailor the platform for customer-specific use cases.Build microservices, tools, and scripts for data preprocessing, feature engineering, and real-time inference.Optimize model serving, caching, and resource utilization for low-latency and high-throughput workloads.Apply hands-on expertise in LangChain, LangWatch, and related Lang ecosystem technologies.Architect enterprise-grade solutions with strong resilience, scalability, and observability.Ensure deployments follow security best practices, including encryption, identity management, and network security.Address compliance requirements such as SOC2, HIPAA, GDPR, and customer-specific regulatory standards.Act as the senior technical lead for customer deployments, resolving complex engineering challenges.Collaborate closely with customer engineering teams to integrate the platform into production systems.Provide critical feedback to product and core engineering teams on performance, scalability, and enterprise integration needs.Create reusable deployment templates, automation scripts, and operational playbooks to accelerate future implementations.Mentor Forward Deploy Engineers on advanced deployment patterns, DevOps practices, and ML systems engineering.Requirements10+ years of experience in software engineering, infrastructure engineering, or applied ML engineering.Strong proficiency in Python, TypeScript/JavaScript, or other backend programming languages.Deep expertise in agent development and agent orchestration techniques.Hands-on experience with LangChain, LangWatch, MCP, Vertex AI, and OpenAI platforms.Proven experience deploying and operating systems on cloud platforms such as AWS, GCP, or Azure using Kubernetes and serverless architectures.Strong understanding of API design, distributed systems, and data engineering workflows.Extensive experience with CI/CD pipelines and infrastructure-as-code tools such as Terraform, Helm, and Ansible.Skilled in implementing monitoring, observability, and alerting solutions using tools like Prometheus, Grafana, ELK, or Datadog.Solid understanding of performance profiling, scaling strategies, and SRE best practices.Knowledge of enterprise SaaS security models, including SSO, RBAC, encryption, and API security.Experience working in regulated environments and compliance frameworks such as SOC2, HIPAA, and GDPR.Excellent debugging and problem-solving skills, especially in high-pressure production environments.Strong communication skills with technical stakeholders, including engineering teams, architects, and senior leadership.Comfortable working in fast-paced, ambiguous environments with minimal guidance.Design and implement robust integration pipelines connecting customer data sources, APIs, and systems of record.Deploy, scale, and monitor machine learning models in production to ensure reliability and performance.Automate deployments using CI/CD pipelines, infrastructure-as-code, and container orchestration technologies such as Docker and Kubernetes.Develop custom extensions, SDKs, and APIs to tailor the platform for customer-specific use cases.Create reusable deployment templates, automation scripts, and operational playbooks to accelerate future implementations.Mentor Forward Deploy Engineers on advanced deployment patterns, DevOps practices, and ML systems engineering.BenefitsA challenging, innovating environment.Opportunities for learning where needed. #J-18808-Ljbffr