Senior Associate – Forward Deployment Engineer (DevOps, AI Deployment)

AI Deployment & DevOps Engineering | Forward Deployed Engineering

Location: Bangalore / Hyderabad

Experience Required

6 – 9 years.

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.