Software Engineer – AI Products

IGNOSIS AI; Ahmedabad District; On-site; INR 1,500,000 - 2,200,000; Full time

Job summary

Ignosis in India seeks a strong software engineer who is AI-native. You will build AI-powered products for financial institutions and their customers, owning problems end to end — from understanding the use case to production, measurement and continuous improvement.

You will evaluate new capabilities and make pragmatic engineering decisions based on cost, latency and quality. AI systems evolve quickly; you should adapt to changing models and tooling, design robust, observable architectures, and

Qualifications

• 3–5 years of software engineering experience building production software.
• 1 year of experience building and operating AI features or AI-native products in production.
• Hands-on experience with LangGraph, LangChain, OpenAI Agents SDK or similar CI frameworks.
• Strong understanding of modern LLM application development and tool use.
• Ability to reason about model trade-offs across quality, latency and cost.
• Strong system design and fundamentals, including APIs, databases and observability.
• Experience with backend or frontend stacks; willingness to own end-to-end product.
• Experience using AI-native development tools such as Claude Code/Codex/Cursor.
• Strong problem-solving ability in ambiguous environments.
• Product thinking with focus on outcomes and pragmatic trade-offs.
• Ownership to take a problem from ambiguity to production readiness.

Responsibilities

• Build and ship AI-native products for BFSI use cases, taking ownership from problem definition to production.
• Translate workflows into reliable AI-powered systems delivering automation and business impact.
• Design and build systems involving LLMs, agents, tool use and retrieval.
• Build and maintain evals to measure quality and prevent regressions.
• Continuously evaluate models and approaches to improve accuracy, cost and latency.
• Design model-agnostic systems and decide which models fit different tasks.
• Develop production-grade AI systems with observability, retries and fail-safes.
• Own the surrounding software — APIs, data flows, UIs, and integrations.
• Assess when not to use an LLM; ensure solid decision boundaries.
• Collaborate with product and engineering to iterate on real-world usage.
• Participate in design and architecture reviews to uphold engineering standards.
• Stay updated on AI developments and apply meaningful advances.

Skills

AI-native development

System design

Production debugging

APIs design

Problem solving

Ownership

Tools

LangGraph

LangChain

OpenAI API

Codex

Cursor

Job description

Ignosis is a well-funded company with a bold vision in the BFSI sector, backed by reputable investors. We are committed to pioneering in financial data intelligence, offering hyper-personalization, automation, and democratized credit solutions. Our mission is to empower the BFSI sector with cutting‑edge technologies and insights. At Ignosis, we’re not just embracing the future; we’re actively shaping it.

Join our dynamic team, where innovation meets excellence, and help us redefine the boundaries of what’s possible in FinTech.

Company Overview

Ignosis is a well-funded company with a bold vision in the BFSI sector, backed by reputable investors. We are committed to pioneering in financial data intelligence, offering hyper-personalization, automation, and democratized credit solutions. Our mission is to empower the BFSI sector with cutting‑edge technologies and insights. At Ignosis, we’re not just embracing the future; we’re actively shaping it.

Join our dynamic team, where innovation meets excellence, and help us redefine the boundaries of what’s possible in FinTech.

Job Overview

We are looking for a strong software engineer who has become AI-native.

You will build AI-powered products that solve real problems for financial institutions and their customers. You will own problems end to end — understanding the use case, choosing the right technical approach, building the product, taking it to production, measuring how well it works, and continuously improving it.

AI systems are evolving quickly. Models, frameworks and approaches that work well today may not be the right choices six months from now. You should be comfortable continuously evaluating new capabilities and making pragmatic engineering decisions based on product quality, accuracy, cost and latency.

Key Responsibilities

• Build and ship AI-native products for BFSI use cases, taking ownership from problem definition to production.
• Translate business workflows into reliable AI-powered systems that deliver measurable outcomes such as automation, user adoption and business impact.
• Design and build systems involving LLMs, agents, tool use, structured outputs, retrieval, document intelligence, Voice AI and other AI capabilities as required by the problem.
• Build and maintain evals for AI use cases to continuously measure quality and prevent regressions.
• Continuously evaluate models and approaches to improve accuracy, quality, cost and latency.
• Design systems that are model‑agnostic where appropriate and make informed decisions about which models are best suited for different tasks.
• Build production‑grade AI systems with appropriate observability, tracing, retries, fallbacks, guardrails and failure handling.
• Own the software surrounding the models — APIs, backend services, data flows, user experiences and integrations required to ship a complete product.
• Think critically about where AI should and should not be used. Not every problem needs an LLM.
• Work closely with product, engineering and business teams to understand customer problems and iterate based on real‑world usage.
• Participate in code reviews, design reviews and architecture discussions, and contribute to maintaining a high engineering bar.
• Keep yourself updated with relevant developments in models, frameworks and AI engineering practices, and bring useful advances into production when they create meaningful value.
• Not restrict yourself to only the above responsibilities and go beyond your role to contribute towards making the organization and business better.

Desired Profile

• 3–5 years of software engineering experience with strong fundamentals in building production software.
• 1 year of experience building and operating AI features or AI‑native products used by customers in production.
• Hands‑on experience building applications using frameworks such as LangGraph, LangChain, OpenAI Agents SDK or similar agent/workflow frameworks.
• Strong understanding of modern LLM application development, including prompting, structured outputs, tool calling, context management, embeddings, retrieval and agentic workflows.
• Ability to reason about different models and their trade‑offs across quality, accuracy, latency and cost.
• Strong system design and software engineering fundamentals, including APIs, databases, distributed systems, observability and production debugging.
• Experience building products using one or more major backend or frontend technology stacks. We are not particular about the programming language or framework.
• Ability and willingness to own a product end to end, even when parts of the stack are outside your primary area of expertise.
• Experience using AI‑native development tools such as Claude Code, Codex, Cursor or similar tools as an integral part of the software development workflow.
• Strong problem‑solving ability and comfort working in environments where the problem, solution and technology may not initially be clearly defined.
• Product thinking — ability to understand the outcome being solved for, identify failure modes and make pragmatic trade‑offs rather than treating model integration as the end goal.
• Ability to reliably turn AI capabilities into useful products rather than treating experimentation or model integration as the final outcome.
• High ownership and the ability to take a problem from an ambiguous starting point to a reliable production system.

Nice To Have

• Experience designing or maintaining AI evaluation systems, including golden datasets, regression evals, human evaluation, LLM‑as‑judge approaches or task‑specific quality metrics.
• Experience working with multiple model providers or migrating workloads between models.
• Exposure to open‑source models and the ecosystem around running or evaluating them.
• Experience with AI observability, tracing and production monitoring.
• Experience building agentic or multi‑step AI workflows.
• Experience working with Voice AI, document intelligence, RAG, extraction, classification or other applied AI systems.
• Experience working in FinTech, BFSI or other regulated industries.
• Understanding of considerations such as PII handling, auditability, hallucination management, human‑in‑the‑loop workflows and deterministic fallbacks in production AI systems.
• Good technical breadth with the ability to evaluate alternative technologies and make appropriate trade‑offs.

What’s in it for you?

• Build AI products that are deployed in real financial workflows rather than isolated demos or experiments.
• Work on problems where AI can directly change how large financial institutions operate and serve their customers.
• Work across a wide range of AI problems as the technology and our products evolve.
• Get to work with some of the largest banks, NBFCs and FinTech players in India and solve their pressing problems.
• Operate in an environment where engineers have significant ownership over both technical decisions and product outcomes.
• Last but not the least, an industry competitive compensation package.

To conclude, this position is tailor‑made for engineers who thrive in dynamic, fast‑paced environments and want to build AI products that create real business outcomes.