Forward Deployed Engineer (Mid-Level)

The work

Your role at ADEL

We’re looking for a hands-on engineer who enjoys building software and working directly with the people who use it. At ADEL, you will turn customer needs into working AI applications, contributing across discovery, development, integration and production deployment. Infrastructure is a core part of the role: you should be comfortable understanding how an application runs, securing its connections and diagnosing problems beyond the code. You will work alongside experienced ADEL engineers and customer teams, with meaningful delivery responsibility at a mid-level. You do not need to have held an FDE title before. This role is remote within India, welcoming applicants from Bengaluru and across the country.

ADEL stands for AI Development and Expertise Labs. We bring senior expertise, embedded delivery and capability transfer to enterprise AI. Your work should help customers move forward with systems they understand and can own.

What you will do

• Understand customer workflows, identify useful AI opportunities and agree on measurable acceptance criteria.
• Build AI-enabled applications, conversational interfaces and agent workflows that connect to enterprise systems.
• Develop APIs, data integrations and user-facing features, troubleshooting across frontend, backend and infrastructure layers.
• Deploy and support applications in a customer’s cloud environment. Configure containers, service connectivity, access permissions and secrets with the customer’s platform team.
• Use CI/CD pipelines, automated tests and safe release or rollback practices to move changes into production.
• Investigate application and infrastructure failures using logs and metrics. Improve monitoring, reliability, response time and operating cost.
• Evaluate AI behaviour against realistic scenarios, including incorrect answers, incomplete data and failed tool calls. Apply appropriate access controls and human review.
• Explain technical choices, demonstrate progress and act on customer feedback. Leave documentation and runbooks that help customer engineers maintain and extend the solution.

What you bring

• 2–5 years of professional experience in software engineering, AI/ML engineering, forward deployed engineering or a closely related hands-on role.
• Strong coding, debugging and testing skills in Python, TypeScript/JavaScript or a comparable production language.
• Practical experience building LLM applications, including retrieval-augmented generation, tool use or agent orchestration. Understand quality, reliability, latency and cost trade-offs.
• Strong understanding of backend and infrastructure systems: APIs, databases, data flow, authentication, authorisation and secure data handling. Be able to trace and debug problems across these layers.
• Hands-on experience deploying and supporting applications on at least one cloud platform, such as AWS, Azure or Google Cloud. Understand compute, storage, networking and identity/access management.
• Confidence using Linux and the command line, with practical networking fundamentals including DNS, HTTP/TLS and service connectivity.
• Practical experience with Docker or equivalent containers, CI/CD pipelines and production logs or monitoring. Be able to diagnose deployment, configuration and access issues.
• Sound software engineering habits, including version control, automated testing and code review.
• Clear communication, customer curiosity and the ability to break an unclear requirement into manageable engineering tasks. You do not need experience with every framework; show us your judgement and how you learn.
• Python OR TypeScript OR JavaScript
• LLM
• RAG
• AI agents
• APIs
• Databases
• Security
• AWS OR Azure OR Google Cloud
• Networking
• Linux
• Docker
• CI/CD
• Monitoring
• Automated testing
• Version control

Useful, but not essential

• Experience with Kubernetes or managed container orchestration, and infrastructure as code using Terraform, Pulumi or an equivalent tool.
• Experience with React or another modern frontend framework.
• Exposure to AI evaluation tools, vector search, observability or data pipelines.
• Customer-facing delivery experience or work within enterprise environments.
• Terraform OR Pulumi
• Kubernetes
• React
• Model evaluation
• Observability
• Customer-facing delivery

What good looks like

• The customer and delivery team agree on the problem, operating constraints and evidence of success.
• A useful AI capability runs reliably in the customer’s environment, with appropriate security, evaluation and monitoring.
• Releases are repeatable, operational issues can be diagnosed and the customer has clear documentation and ownership.

Show us the evidence.

Describe a system you helped build and deploy. What was the customer problem, what did you personally contribute, and which infrastructure or production issue did you resolve? Explain the result. An anonymised example is welcome; do not share confidential code or information.

We welcome relevant experience from different career paths. Share what you personally contributed; an anonymised example is welcome when your work is confidential.