Lead Forward Deployed AI Engineer

Experience: 5-10 Years

About Role

The Lead Forward Deployed AI Engineer leads small, high-agency teams solving important customer and product problems with AI. You will often begin with an ambiguous question - can AI materially change this workflow? - and turn it into a narrow problem, a working build and measurable proof.

You are expected to combine deep engineering judgement with customer and business understanding. You should be comfortable working with senior stakeholders, making architecture choices quickly, leading the build and identifying which parts should become reusable Azentio IP.

This is a hands-on technical leadership role. The objective is not to grow large delivery teams; it is to repeatedly turn difficult problems into production AI capabilities and make the next deployment faster.

Responsibilities

Problem Framing & Customer Discovery

- Lead discovery with customer business and technology leaders to understand the workflow, constraints, and desired outcome.
- Identify the narrowest high-value wedge that can be proven quickly.
- Define business and technical success measures before significant build effort begins.
- Challenge requirements where AI is not the right solution or where a simpler deterministic approach is better.

Technical Leadership & Architecture

- Own solution architecture across application, AI, agent, data, API, security, and deployment layers.
- Decide where models, agents, rules, workflows, APIs, and humans belong in the solution.
- Lead design and implementation of production-grade agentic systems and AI-native applications.
- Set engineering standards for code quality, evaluation, observability, security, and safe tool use.




- Remain hands-on enough to review code, traces, architecture, and difficult failures directly.

Forward Deployment & Production

- Lead small teams from prototype through integration, real-user deployment, and measurable proof.
- Work directly with customer technology teams on APIs, data, identity, workplace, and production constraints.
- Own technical resolution of issues spanning application, integration, data, infrastructure, and model behaviour.
- Ensure production solutions are supportable, secure, observable, and maintainable.

Productisation & Reuse

- Separate customer-specific last-mile work from reusable domain and platform capabilities.
- Identify patterns that should become common connectors, tools, services, agent capabilities, or product features.
- Feed field learning into Product, Architecture and AI platform priorities.
- Stop weak ideas early and accelerate solutions that show evidence of repeatability and customer value.

Team Leadership

- Lead and mentor Forward Deployed AI Engineers and Senior FDEs.
- Create a strong engineering culture of rapid experimentation, disciplined measurement, and production ownership.
- Partner with Product, Engineering, Sales, and customer stakeholders without becoming a coordination layer.

Required Technical Skills

Software & Systems Engineering





- Expert-level Python engineering and strong working knowledge of Java.
- Strong software design fundamentals, code quality practices, testing, debugging, and performance engineering.
- Experience designing distributed, cloud-native, and microservices-based systems.

Enterprise Integration & Data

- Strong API and integration architecture, including identity, authorization, events, and enterprise application integration.
- Strong SQL and relational database understanding across PostgreSQL, Oracle, MySQL, or similar platforms.
- Ability to design data flows and interfaces that support reliable AI applications.

AI & Agentic Architecture

- Deep experience with LLM applications, RAG, tool use and agent orchestration.
- Strong understanding of context management, evaluation, model selection, failure modes, and bounded autonomy.
- Ability to design multi-agent approaches where they provide genuine value rather than unnecessary complexity.
- Experience with model gateways, observability, security, and cost/latency trade-offs.

Production & Cloud

- Strong experience with cloud platforms, containers, Kubernetes, and CI/CD.
- Experience designing production monitoring, tracing, incident handling, and operational controls for AI-enabled systems.

Preferred

- BFSI product or technology experience across lending, banking, insurance, or financial crime.
- Experience leading customer-facing engineering or forward-deployed teams.
- Demonstrated ability to turn customer deployments into reusable product capabilities.
- Experience influencing both technical architecture and product/business decisions.

📌 Forward Deployed Software Engineer (Bengaluru)
🏢 Hack And Grow Consultancy
📍 Bengaluru