Manager Forward Deployment Engineer (DevOps, AI Deployment)

AI Deployment & DevOps Engineering | Forward Deployed Engineering

Experience Required

9 to 16 years, including team and delivery leadership.

Job Summary

An engineering leader who owns how AI solutions are deployed into enterprise environments across a set of accounts. You will own delivery, standards, and the customer relationship, and set the direction for building, securing, and operating AI applications in production on AWS.

Key Responsibilities

- Own the delivery of AI deployment work across your accounts, including scope, quality, timelines, and standards.
- Set direction and standards for CI/CD, Infrastructure as Code, cloud architecture, security, and MLOps/LLMOps.
- Lead the responsible integration of AI solutions into legacy and regulated enterprise environments at scale.
- Act as senior escalation and decision-maker on complex or high-stakes deployments.
- Build reusable accelerators and patterns that raise deployment quality across the practice.
- Build, lead, and grow the team through hiring, coaching, and development.
- Own senior customer relationships and communicate progress, risks, and value.
- Surface delivery friction and product gaps to leadership as structured field intelligence.

Required Qualifications

- Extensive DevOps or platform engineering experience, including leading teams and owning delivery at scale.
- Current depth in AWS, Kubernetes, CI/CD, and Infrastructure as Code.




- Proven enterprise integration experience with security, identity, and governance constraints.
- Proven ownership of delivery outcomes and senior customer relationships.
- A clear strategy for productionising AI at scale (LLMOps/MLOps), including responsible AI, backed by delivery experience.
- Strong leadership and communication skills; decisive under pressure.
- Mandatory: AWS, DevOps, or GenAI certification (at least one) is required.

Preferred Qualifications

- AWS enterprise-scale architecture experience.
- Enterprise AI and data platforms (Palantir Foundry, Databricks, Snowflake).
- A background in consulting or managed services.
- Experience in regulated industries.
- A leadership or delivery certification (e.g., PMP, Protected).
- AWS Certified Solutions Architect Professional and/or AWS Certified DevOps Engineer Professional.

Technical Skills & Tools

- Cloud & platform: AWS (Bedrock, SageMaker, EKS, Lambda, IAM), Kubernetes
- Delivery & IaC: CI/CD (GitHub Actions, GitLab CI, ArgoCD), Terraform
- AI productionisation: LLMOps / MLOps, model serving, evaluation, responsible AI, governance
- Security & compliance: IAM, secrets management, network security, data governance
- Good to have: Enterprise AI and data platforms (Palantir Foundry, Databricks, Snowflake), MLflow

Soft Skills & Competencies

- Leadership of both a team and a customer relationship.
- Stakeholder and customer management.
- Decisive under pressure.
- Coaching and people development.

📌 Manager _Forward Deployment Engineer , DevOps, AI Deployment (Hyderabad)
🏢 PwC
📍 Hyderabad