Lead AI Software Engineer

InvestCloud, Inc.; Bengaluru; Hybrid; INR 1,800,000 - 3,000,000; Full time

Job summary

InvestCloud, Inc. in Bengaluru is seeking a senior AI/ML engineer to lead production-grade LLM applications and agent platforms. You will design scalable patterns for stateful agents, tool calls, RAG, and memory, while collaborating with product, security, and data teams to ensure robust, auditable systems.

You will shepherd prototypes to production with CI/CD, observability, and incident response, mentoring engineers and delivering high-quality APIs and services. Hybrid work is supported.

Qualifications

• Typically 8+ years in software engineering, ML/AI engineering, or related field.
• Delivering production AI/LLM systems with ownership from design to operation.
• Strong Python and production-grade service/API experience.
• Experience with CI/CD, observability, and production deployment workflows.
• Familiarity with relational databases and data quality considerations.

Responsibilities

• Define reusable architectures for stateful agents, tool calling, memory, human-in-the-loop workflows, model orchestration, RAG, and vector retrieval.
• Design, build, deploy, and operate production-grade LLM applications, agent services, integrations, retrieval layers, and tool adapters using Python and modern engineering practices.
• Lead architecture and design reviews; make pragmatic trade-offs across quality, latency, reliability, cost, maintainability, data access, and security.
• Own the transition from prototype to production, including CI/CD, observability, incident response, rollback, and continuous improvement.
• Partner with global AI, product, engineering, data platform, security, legal, and compliance teams to shape the roadmap and drive adoption of common AI building blocks.
• Mentor senior engineers and data scientists through design reviews, pair engineering, reference implementations, documentation, and operational playbooks.
• Evaluate models, frameworks, platforms, and third-party providers; lead technical due diligence and production-readiness assessments.
• Define evaluation and regression strategies for task success, factuality, groundedness, safety, robustness, and agent behavior.

Skills

Python

English communication

Leadership

Tools

Git

Docker

LangGraph

LangChain

PostgreSQL

Oracle

Job description

Core Responsibilities

• Define reusable architectures and patterns for stateful agents, tool calling, memory, human-in-the-loop workflows, model orchestration, RAG, and vector retrieval.
• Design, build, deploy, and operate production-grade LLM applications, agent services, integrations, retrieval layers, and tool adapters using Python and modern engineering practices.
• Lead architecture and design reviews; make pragmatic trade-offs across quality, latency, reliability, cost, maintainability, data access, and security.
• Own the transition from prototype to production, including CI/CD, observability, incident response, rollback, and continuous improvement.

Agentic-AI architecture and production engineering

• Define reusable architectures and patterns for stateful agents, tool calling, memory, human-in-the-loop workflows, model orchestration, RAG, and vector retrieval.
• Design, build, deploy, and operate production-grade LLM applications, agent services, integrations, retrieval layers, and tool adapters using Python and modern engineering practices.
• Lead architecture and design reviews; make pragmatic trade-offs across quality, latency, reliability, cost, maintainability, data access, and security.
• Own the transition from prototype to production, including CI/CD, observability, incident response, rollback, and continuous improvement.

Evaluation, security, and responsible AI

• Define evaluation and regression strategies for task success, factuality, groundedness, safety, robustness, and agent behavior.
• Establish tracing, monitoring, feedback, and service-level measures for quality, availability, latency, and cost per task.
• Design controls for privacy, PII, access management, prompt injection, data leakage, unsafe tool use, auditability, and human override.

Technical leadership and collaboration

• Partner with global AI, product, engineering, data platform, security, legal, and compliance teams to shape the roadmap and drive adoption of common AI building blocks.
• Mentor senior engineers and data scientists through design reviews, pair engineering, reference implementations, documentation, and operational playbooks.
• Evaluate models, frameworks, platforms, and third-party providers; lead technical due diligence and production-readiness assessments.

Required Qualifications And Experience

• Typically, 8 + years in software engineering, machine learning engineering, AI engineering, or a related discipline; equivalent depth will be considered.
• At least 3-4 years of delivering production AI, Generative AI, machine learning, or LLM-powered systems.
• Demonstrated ownership of production LLM applications, agentic workflows, RAG systems, or AI-enabled services from design through operation.
• Strong Python skills and experience building maintainable, tested, production-quality services and APIs.
• Hands-on experience with LangGraph, LangChain, or comparable agentic frameworks, including state, tools, memory, and human-in-the-loop patterns.
• Strong understanding of prompt/context engineering, embeddings, vector search, retrieval, and LLM evaluation.
• Experience with Git, Docker, CI/CD, cloud services, observability, and production deployment workflows.
• Working knowledge of PostgreSQL, Oracle, or comparable relational databases, including data quality, access, lineage, and performance considerations.
• Ability to lead technical decisions across teams without formal authority, communicate clearly in English, and operate with sound judgment in a controls-focused environment.

Preferred Qualifications

• Experience with AWS, GCP, Azure, Snowflake, Databricks, vector databases, model gateways, tracing, evaluation, or guardrail tooling.
• Experience operating AI systems in financial services or another regulated industry; knowledge of model risk, privacy, and secure software development.
• Background in MLOps, platform engineering, event-driven systems, distributed services, or high-availability production environments.
• Data science or machine-learning experience with PyTorch, scikit-learn, or equivalent frameworks.
• Practical experience with agentic coding tools such as Claude Code, Cursor, or comparable tools, with continued ownership of code quality, security, testing, and design.

Working model and location

Based in InvestCloud's Bengaluru office, working closely with global colleagues across locations. Hybrid working model with 3 days in office.

Why InvestCloud

• Shape a strategic agentic-AI capability in a global wealth-technology company.
• Own technical decisions from architecture through production operation across multiple products.
• Work across AI engineering, data science, product engineering, security, and financial-services technology.
• Join a diverse, international, and cross-functional engineering environment.

Equal opportunity

InvestCloud is committed to fostering an inclusive workplace and welcomes applicants from all backgrounds.