Forward-Deployed Engineer

Role overview

This opportunity is for a forward-deployed engineer who can sit close to business stakeholders, turn loosely defined product ideas into working prototypes, and help small engineering pods move quickly through build, demo, and feedback loops. The role calls for a strong hands-on builder with broad full-stack capability, practical GenAI experience, and the judgment to convert strategy into shippable prototype outcomes. For more senior profiles, the position also includes mentoring, architecture direction, and ownership of stakeholder-facing solutions.

Key responsibilities

• Work directly with business subject-matter experts and product stakeholders to clarify problems and identify strong prototype use cases.
• Build rapid prototypes across the frontend, backend, APIs, data integrations, cloud setup, and AI workflows.
• Turn product direction and unclear requirements into technical designs and functional demos.
• Drive a daily cycle of building, demonstrating, collecting feedback, and refining with an execution-first approach.
• Guide a small team of 4 to 5 engineers and help the pod operate with a high level of independence.
• Provide technical direction on architecture, API structure, data modelling, cloud deployment, AI integration, and security.
• Assess feasibility, highlight risks, balance trade-offs, and recommend practical implementation approaches.
• Create prototype solutions that can be evaluated for pilot funding and later transitioned into a delivery stream if approved.
• Contribute to hiring by supporting technical screening, whiteboarding, and prototype-based evaluations.

Core capability areas

Frontend: React.js, TypeScript, Next.js, JavaScript, single-page application architecture, responsive interfaces, API integration, Redux, Zustand, React Query, Tailwind CSS, Material UI, and Ant Design.

Backend: Python, FastAPI, REST APIs, Node.js, GraphQL, authentication, authorization, Celery, asynchronous processing, and service-oriented design.

AI / GenAI: Generative AI, LLMs, prompt design, RAG, embeddings, vector search, agentic AI, LangChain, LlamaIndex, OpenAI APIs, Azure OpenAI, tool calling, guardrails, and observability.

Cloud & infrastructure: AWS services such as Lambda, API Gateway, S3, RDS, Aurora, IAM, CloudWatch, ECS, Fargate, Kubernetes, EKS, EventBridge, SQS, SNS, Terraform, AWS CDK, and VPC.

Databases: PostgreSQL, SQL, MongoDB, Redis, DynamoDB, OpenSearch, Pinecone, pgvector, data modelling, and data integration.

DevOps: Docker, Git, CI/CD, GitHub Actions, GitLab CI, automated testing, logging, monitoring, and environment management.

Architecture: System design, API design, microservices, event-driven systems, scalable solutions, security architecture, and platform integration.

Product engineering: Product development, rapid prototyping, MVP builds, greenfield engineering, full-stack development, and innovation-focused delivery.

Soft skills: Stakeholder management, business analysis, critical thinking, problem solving, communication, requirement discovery, ambiguity handling, and agile working style.

Must-have capabilities

• Strong full-stack engineering ability across frontend, backend, data, cloud, and deployment layers.
• Practical experience building or integrating AI/GenAI features, not only theoretical knowledge.
• Ability to quickly create usable prototypes and improve them using stakeholder feedback.
• Sound architecture judgment with the ability to choose pragmatic designs that can still scale later.
• Comfort working directly with business SMEs, asking the right questions, and setting realistic expectations.
• For senior candidates, capability to mentor engineers, review designs, and guide execution without reducing speed.
• Ability to begin from a blank slate and move an idea into a working product direction.

Experience profile

Profiles with around 4 to 8 years of relevant experience are preferred for the FDE track, especially those who have strong hands-on product engineering experience, AI exposure, and the ability to independently own prototype components.

A product engineering background is required. Experience in startups, greenfield builds, or innovation labs is considered a strong advantage.

Preferred background

• Experience in product engineering or full-stack software development.
• Track record of building greenfield products, MVPs, internal platforms, prototypes, or customer-facing product features.
• Exposure to AI assistants, automation workflows, RAG, agentic AI, or LLM-powered applications.
• Prior work in startups, product teams, innovation groups, or incubation environments.
• Comfort using tools such as Palantir, Replit, AI coding assistants, and rapid development platforms.
• For lead-level applicants, background as an architect, tech lead, incubation lead, or pod lead is preferred.
• Based in Hyderabad or open to working from Hyderabad.

Working model

• The team collaborates directly with business SMEs and does not follow a conventional requirement-heavy delivery model.
• The daily operating rhythm is sprint-like: identify the requirement, build a prototype, demo it, gather feedback, and refine it.
• Pods usually consist of 4 to 5 engineers and are expected to be about 70% self-sufficient, with the remaining 30% supported by the incubation lead.
• Prototype outputs are not expected to be production-ready immediately; only successful pilots are assessed for funding before moving into delivery.
• Soft skills carry as much weight as technical ability, especially critical thinking, communication, stakeholder engagement, and comfort with ambiguity.

Eligibility

Any graduate can apply.

Location

Hyderabad, India.