Job Description :

Senior Associate Forward Deployment Engineer (DevOps, AI Deployment) - AI Deployment & DevOps Engineering | Forward Deployed Engineering

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.

Location & Work Model:

- Location: Bengaluru or Hyderabad.

- Work model: Forward-deployed and customer-facing; embedded within an enterprise client's team.

- Working hours: Overlap with client business hours (including US / EST), with occasional deployment support