Forward Deployed Engineer

Requirements<\/h3>

• 10+ years of experience in software engineering, infrastructure engineering, or applied ML engineering.<\/span>
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• Strong proficiency in Python, TypeScript\/JavaScript, or other backend programming languages.<\/span>
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• Deep expertise in agent development and agent orchestration techniques.<\/span>
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• Hands\-on experience with LangChain, LangWatch, MCP, Vertex AI, and OpenAI platforms.<\/span>
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• Proven experience deploying and operating systems on cloud platforms such as AWS, GCP, or Azure using Kubernetes and serverless architectures.<\/span>
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• Strong understanding of API design, distributed systems, and data engineering workflows.<\/span>
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• Extensive experience with CI\/CD pipelines and infrastructure\-as\-code tools such as Terraform, Helm, and Ansible.<\/span>
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• Skilled in implementing monitoring, observability, and alerting solutions using tools like Prometheus, Grafana, ELK, or Datadog.<\/span>
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• Solid understanding of performance profiling, scaling strategies, and SRE best practices.<\/span>
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• Knowledge of enterprise SaaS security models, including SSO, RBAC, encryption, and API security.<\/span>
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• Experience working in regulated environments and compliance frameworks such as SOC2, HIPAA, and GDPR.<\/span>
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• Excellent debugging and problem\-solving skills, especially in high\-pressure production environments.<\/span>
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• Strong communication skills with technical stakeholders, including engineering teams, architects, and senior leadership.<\/span>
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• Comfortable working in fast\-paced, ambiguous environments with minimal guidance.<\/span>
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• Lead end\-to\-end technical deployments of the PHTN.ai platform within enterprise environments.<\/span>
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• Design and implement robust integration pipelines connecting customer data sources, APIs, and systems of record.<\/span>
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• Deploy, scale, and monitor machine learning models in production to ensure reliability and performance.<\/span>
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• Automate deployments using CI\/CD pipelines, infrastructure\-as\-code, and container orchestration technologies such as Docker and Kubernetes.<\/span>
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• Develop custom extensions, SDKs, and APIs to tailor the platform for customer\-specific use cases.<\/span>
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• Build microservices, tools, and scripts for data preprocessing, feature engineering, and real\-time inference.<\/span>
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• Optimize model serving, caching, and resource utilization for low\-latency and high\-throughput workloads.<\/span>
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• Apply hands\-on expertise in LangChain, LangWatch, and related Lang ecosystem technologies.<\/span>
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• Architect enterprise\-grade solutions with strong resilience, scalability, and observability.<\/span>
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• Ensure deployments follow security best practices, including encryption, identity management, and network security.<\/span>
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• Address compliance requirements such as SOC2, HIPAA, GDPR, and customer\-specific regulatory standards.<\/span>
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• Act as the senior technical lead for customer deployments, resolving complex engineering challenges.<\/span>
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• Collaborate closely with customer engineering teams to integrate the platform into production systems.<\/span>
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• Provide critical feedback to product and core engineering teams on performance, scalability, and enterprise integration needs.<\/span>
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• Create reusable deployment templates, automation scripts, and operational playbooks to accelerate future implementations.<\/span>
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• Mentor Forward Deploy Engineers on advanced deployment patterns, DevOps practices, and ML systems engineering.<\/span><\/span><\/li> <\/ul>
  
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  Benefits<\/h3>

  • A challenging, innovating environment.<\/span><\/span>
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  • Opportunities for learning where needed.<\/span><\/span>
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