Senior Associate Forward Deployment Engineer – DevOps & AI

Job Description

Job Summary :

A senior DevOps engineer who owns how AI solutions are deployed into a client’s environment. As the technical owner for deployment, you will design pipelines and infrastructure, harden AI applications for production, and meet enterprise security and governance requirements on AWS.

Key Responsibilities :

– Own the deployment architecture for AI solutions on AWS.

– Design and own CI/CD, Infrastructure as Code, and release standards across engagements.

– Lead integration of AI solutions into legacy and regulated environments, respecting identity, security, and governance.

– Set up scalable model and agent serving, with the vector and retrieval infrastructure behind it.

– Establish observability, evaluation, and cost controls for AI workloads in production.

– Define a practical approach to security, governance, and responsible AI for deployments.

– Build reusable deployment accelerators, and mentor engineers.

– Bring field learnings and product gaps back to the wider practice.

Required Qualifications:

– Substantial DevOps or platform engineering experience with ownership of production deployments.

– Deep CI/CD, Docker, and Kubernetes experience, with strong Terraform / IaC.

– Strong AWS fluency across deployment-relevant services.

– Strong grounding in identity, security, and networking, and enterprise integration.

– Solid automation skills and a habit of codifying build and run processes.

– Deep, hands-on experience deploying LLM and agentic applications to production (LLMOps), including serving, scaling, retrieval infrastructure, observability, evaluation, and responsible AI.

Preferred Qualifications:

– Enterprise AI platforms (Palantir Foundry, Databricks, Snowflake) and MLOps tooling at scale.

– Experience in regulated industries.

– SRE or reliability experience.

– Prior consulting, customer success, or forward-deployed work.

– AWS Certified DevOps Engineer Professional and/or AWS Certified Solutions Architect Professional; CKA or a cloud AI/ML certification.

Technical Skills & Tools:

– Cloud (AWS): Bedrock, SageMaker, Lambda, ECS, EKS, Step Functions, S3, API Gateway, IAM, CloudWatch

– Containers & IaC: Docker, Kubernetes, Helm, Terraform (modules), Ansible

– CI/CD: GitHub Actions, GitLab CI, Jenkins, ArgoCD (GitOps)

– AI deployment (LLMOps): model and agent serving and scaling, RAG & vector databases, evaluation, prompt versioning

– Observability & cost: OpenTelemetry, Langfuse, Prometheus, Grafana

– Security & governance: IAM, secrets management, network security, responsible-AI controls

– Scripting: Python, Go, Bash

– Good to have: MLOps at scale (MLflow, model registries, feature stores), Databricks, Snowflake, Palantir Foundry

Soft Skills & Competencies:

– Takes ownership of deployment outcomes.

– Clear communication with client stakeholders.

– Mentors engineers and sets standards.

– Sound judgement on security, governance, and responsible AI.

Experience Required:

– 6-9 years.

Reporting & Team:

– Embedded with an enterprise customer as the technical owner for deployment, within our Forward Deployed Engineering practice; mentors engineers on the team.

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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