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Forward Deployment Engineer; DevOps, AI Deployment
Job Description & How to Apply Below
Location: Bengaluru
Associate – Forward Deployment Engineer (Dev Ops, AI Deployment)
AI Deployment & Dev Ops Engineering | Forward Deployed Engineering
Location:
Bangalore / Hyderabad
Experience Required
3 –6 years.
Job Summary
A Dev Ops engineer focused on getting AI solutions into real production. Embedded within an enterprise client's team, you will take AI applications from prototype to reliable, secure production on AWS, using strong CI/CD, containerisation, and infrastructure automation.
Key Responsibilities
Package and deploy AI/LLM applications and agent workflows onto the client's AWS infrastructure.
Build and maintain CI/CD pipelines that ship AI services safely and repeatably.
Containerise services with Docker and run them on Kubernetes.
Provision infrastructure as code with Terraform, integrating the required identity, security, and networking.
Set up model and application serving, including AWS Bedrock integrations and vector database infrastructure.
Add observability and cost tracking for AI workloads.
Integrate AI solutions into existing legacy systems and regulated data environments.
Automate repetitive deployment and operations tasks.
Collaborate with client engineers over Teams, Slack, and email, and keep deployment runbooks current.
Required Qualifications
Proven Dev Ops, platform, or deployment engineering experience shipping to production.
Strong CI/CD and release-automation experience.
Hands-on production experience with Docker and Kubernetes.
Infrastructure as Code experience and strong AWS fluency.
Scripting ability for automation (Python and/or Bash).
Ability to integrate into a client's existing identity, security, and networking setup.
A real understanding of how AI and LLM applications are deployed and run in production, including model serving and RAG/vector infrastructure.
AWS Certified Solutions Architect – Associate or AWS Certified Dev Ops Engineer – Associate.
Preferred Qualifications
Deploying inside regulated environments with governance and change-management overhead.
Enterprise AI or data platforms such as Databricks, Snowflake, or Palantir Foundry.
MLOps tooling (MLflow, model registries, feature stores).
Some SRE or reliability experience.
Prior customer-facing or forward-deployed work.
Certified Kubernetes Administrator (CKA) or a Terraform Associate certification.
Technical Skills & Tools
Cloud (AWS): Bedrock, Sage Maker, Lambda, ECS, EKS, Step Functions, S3, API Gateway, IAM, Cloud Watch
Containers & IaC: Docker, Kubernetes, Helm, Terraform, Ansible, Cloud Formation
CI/CD: Git Hub Actions, Git Lab CI, Jenkins, ArgoCD
AI deployment: LLM/agent serving, inference endpoints, RAG infrastructure, vector databases (Pinecone, pgvector, Weaviate, Qdrant, Open Search)
Observability & cost: Open Telemetry, Langfuse, Prometheus, Grafana, Cloud Watch
Security & networking: IAM, secrets management, VPC and network configuration
Scripting: Python, Bash, Git
Good to have: MLOps (MLflow, model registries, feature stores), Databricks, Snowflake, Palantir Foundry
Soft Skills & Competencies
Takes ownership of deployment outcomes.
Clear communication with client engineers.
Comfortable working within enterprise security and governance constraints.
Adaptable and delivery-focused.
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