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
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