Job Description
Senior Associate – Forward Deployment Engineer (DevOps, AI Deployment)
AI Deployment & DevOps Engineering | Forward Deployed Engineering
Experience Required
5–9 years.
Location: Bangalore / Hyderabad
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.
- Mandatory: AWS, DevOps, or GenAI certification (at least one) is required.
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