Forward Deployment Engineer - AI Solutions

ValuEnable; Mumbai; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

ValuEnable Pvt Ltd is seeking a hands-on Forward Deployment Engineer to lead design, build, deployment, and continuous improvement of AI-led insurance solutions. You will work at the intersection of engineering, applied AI, client problem solving, and production deployment to deliver end-to-end workflows across voice AI, document intelligence, BI extraction and workflow automation.

You will collaborate with domain experts, client teams, and product owners to translate complex business problems

Qualifications

• 3 to 7 years of hands-on engineering experience with AI focus.
• Experience deploying production AI/data/API-led workflows.

Responsibilities

• Understand client workflows, problems, data availability, and integration constraints.
• Convert requirements into technical architecture, design, and delivery plans.
• Build AI-enabled applications using LLMs, RAG, vector databases, APIs and cloud components.
• Integrate AI components like speech-to-text, OCR, databases, APIs and enterprise platforms.
• Develop prototypes and scale into production-ready systems with reliability and security.
• Define testing and evaluation approaches for AI outputs and workflows.
• Create feedback loops and monitoring for continuous improvement.
• Coordinate with client IT, business and product teams during pilots and deployments.
• Provide production support and issue diagnosis for deployed systems.
• Capture learnings to reusable VE frameworks and accelerators.

Skills

Applied AI

GenAI

Problem solving

Client handling

End-to-end solutioning

Tools

APIs

Cloud infrastructure

Distributed systems

Vector databases

LLMs

LangChain

React (basic)

Job description

Company Name: ValuEnable Pvt Ltd (A Zerodha backed Insurtech)

Position: Forward Deployment Engineer, AI Solutions

VE is looking for a hands-on Forward Deployment Engineer to lead the design, build, deployment, and continuous improvement of AI-led solutions for insurance clients. This role will sit at the intersection of engineering, applied AI, client problem solving, production deployment, and product development.

The ideal candidate should be able to understand ambiguous business problems, quickly identify the AI and technology components required, build working prototypes, and convert them into production-ready workflows. The role will involve working closely with domain experts, client teams, product owners, and internal engineers to develop solutions across areas such as voice AI, agent assist, document intelligence, product configurators, BI extraction, QA analytics, and insurance workflow automation.

This role is meant for someone who can deliver end-to-end solutions rather than isolated proof-of-concepts. The candidate should be comfortable moving from problem discovery to architecture, prototype, deployment, user feedback, production support, and continuous improvement.

Major Responsibilities

• Understand client workflows, business problems, data availability, and integration constraints
• Convert business requirements into technical architecture, solution design, and delivery plans
• Build AI-enabled applications using LLMs, RAG, vector databases, APIs, workflow orchestration, databases, and cloud-based components
• Integrate AI components such as speech-to-text, text-to-speech, LLMs, OCR, dialers, databases, enterprise APIs, and third-party platforms
• Develop fast prototypes and harden them into scalable, secure, reliable, and production-ready systems
• Design evaluation frameworks for AI solutions, including accuracy, latency, cost, reliability, and business impact metrics
• Define testing approaches for AI outputs before deployment, including hallucination checks, extraction quality reviews, response consistency checks, and workflow-level validation
• Create human review workflows, feedback loops, and monitoring mechanisms to continuously improve solution performance
• Monitor hallucinations, extraction quality, model performance, response quality, latency, and production failures over time
• Work iteratively with insurance domain experts to improve solution accuracy, usability, and business impact
• Coordinate with client IT, business, operations, and vendor teams during pilots and production deployments
• Support deployed systems used by internal or external customers, including issue diagnosis, performance monitoring, logging, and improvement cycles
• Capture learnings from deployments and convert them into reusable VE frameworks, templates, accelerators, and product components

Why Should You Consider Working (In this Role!) With ValuEnable?

• We are a 5 year old well-funded and profitable service focused insurtech venture, solving a USD 40 Bn per year customer retention problem for insurers. We work with 8 of the top 10 private Life insurers in the country helping them solve this problem.
• We have been recognised by IRDAI in its first-ever Open House for Insurtech held last year. One of India's most well respected financial institute, Zerodha, is our lead institutional investor through it's fintech investment arm, Rainmatter Fintech Investment
• You will work with significantly senior levels at our partners’ end, helping address their specific requirements.
• You will be handling interactions with multiple stakeholders such as business, technology and product teams. You can leverage the experience you already have, we promise your experience will only be enriched!

Required Experience:

• 3 to 7 years of hands‑on engineering experience, with at least 1 to 2 years in applied AI, ML, GenAI, automation, or data‑driven product workflows
• Prior roles could include AI Engineer, ML Engineer, Backend Engineer with AI exposure, Solutions Engineer, Full‑stack Engineer in AI products, Product Engineer, or Technical Product Engineer
• Previous tech product managers may be considered only if they have been deeply involved in technical solutioning and can independently build or prototype AI/data/API‑led workflows
• Experience deploying and supporting production systems used by internal or external customers is strongly preferred
• Experience working with APIs, databases, cloud infrastructure, and distributed systems is required
• Preference will be given to candidates who have built working systems involving LLMs, APIs, data pipelines, RAG, workflow automation, enterprise integrations, or production‑grade AI applications
• Candidates should have experience delivering end‑to‑end solutions, not just isolated POCs, demos, notebooks, or experimental prototypes

Good to Have:

• Experience in BFSI, insurance, call center technology, document automation, voice AI, agent assist, QA automation, or enterprise SaaS deployment
• Familiarity with platforms such as OpenAI, Anthropic, Gemini, AWS Bedrock, Azure AI, Deepgram, ElevenLabs, LangChain, LlamaIndex, vector databases, and cloud infrastructure
• Basic frontend capability using React, Next.js, Streamlit, or similar tools
• Understanding of PII handling, auditability, access control, enterprise security, responsible AI practices, and production governance Indicative Skill Mix:
• Applied AI and GenAI workflows: 25%
• Business problem solving: 15%
• Client and stakeholder handling: 10%

Ideal Candidate: The ideal candidate is a practical AI engineer who enjoys solving real business problems, can build hands‑on, works well with domain experts, and is comfortable moving from discovery to prototype to production

The candidate should be able to take a broad business problem, such as improving lapse calling, automating BI extraction, building agent assist, or creating a document intelligence workflow, and independently break it down into the required data, AI components, APIs, architecture, evaluation approach, implementation plan, deployment model, and feedback loop.

The candidate should not only deliver client‑specific solutions but also help VE build reusable AI assets that strengthen future deployments. This includes reusable evaluation frameworks, extraction templates, prompt patterns, integration playbooks, monitoring approaches, and deployment learnings