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Deployment Engineer With ML/ AI

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Job Description:

1. Deployment Engineering:


•Lead the end-to-end technical implementation of the Agentic platform in enterprise environments.


•Design and build robust integration pipelines, connecting customer data sources, APIs, and systems of record to the platform.


•Deploy and scale machine learning models in production, ensuring performance, reliability, and monitoring.


•Automate deployments using CI/CD pipelines, infrastructure-as-code, and container orchestration (Docker, Kubernetes).

2. AI Platform Integration & Optimization:


•Implement custom extensions, SDKs, and APIs to adapt the platform to customer-specific use cases.


•Build tools, scripts, and microservices to handle data preprocessing, feature engineering, and real-time inference.


•Optimize model serving, caching, and resource allocation for low-latency, high-throughput environments.

3. Reliability, Security & Compliance:


•Architect solutions that meet enterprise-grade standards for resilience, observability, and scalability.


•Ensure deployments adhere to security best practices (encryption, identity management, network security).


•Navigate compliance requirements such as SOC2, HIPAA, GDPR, and customer-specific regulatory constraints.

4. Engineering Leadership & Technical Escalation:


•Serve as the senior technical lead on customer deployments, resolving complex engineering challenges.


•Partner closely with customer engineering teams to embed the platform into production workflows.


•Provide critical field feedback to product and core engineering teams on performance, scaling, and enterprise integration needs.

5. Enablement & Knowledge Sharing:


•Create reusable deployment templates, automation scripts, and playbooks to accelerate future projects.


•Mentor other Forward Deploy Engineers on advanced deployment patterns, DevOps practices, and ML systems engineering.

Qualifications

Engineering Expertise:

  • Good years in software engineering, infrastructure engineering, or applied ML engineering.
  • Strong proficiency in Python, TypeScript/JavaScript, or other backend languages.
  • Experience deploying systems on cloud platforms (AWS, GCP, Azure) using Kubernetes and serverless frameworks.
  • Deep understanding of API design, distributed systems, and data engineering workflows.
  • Hands-on experience operationalizing ML models in production (TensorFlow, PyTorch, Hugging Face, or custom inference engines).

DevOps & Infrastructure:

  • Strong background in CI/CD pipelines, infrastructure as code (Terraform, Helm, Ansible).
  • Skilled in setting up monitoring, observability, and alerting (Prometheus, Grafana, ELK, Datadog).
  • Familiarity with performance profiling, scaling strategies, and SRE principles.

Security & Compliance Awareness:

  • Knowledge of enterprise SaaS security models (SSO, RBAC, encryption, API security).
  • Experience working in environments subject to compliance frameworks (SOC2, HIPAA, GDPR).

Soft Skills:

  • Excellent debugging and problem-solving in high-pressure deployment environments.
  • Strong communication with technical stakeholders (engineering teams, architects, CTOs).
  • Comfort working in fast-moving, ambiguous situations with minimal guidance