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This role is for one of our clients
Industry: Technology, Information and Media
Seniority level: Mid-Senior level
Min Experience: 3 years
Location: Mumbai
JobType: full-time
₹15,00,000 - ₹25,00,000 a year
We’re scaling our enterprise AI capabilities and are looking for an Applied AI Solutions Engineer to help design, build, and operationalize advanced AI systems already being used by leading financial institutions. This role sits at the intersection of AI research and real-world production—focused on delivering reliable, secure, and scalable AI solutions for BFSI-grade environments.
You’ll work closely with AI researchers, platform engineers, and financial domain experts to turn cutting-edge LLM and agent-based concepts into robust enterprise applications.
What You’ll Do
Enterprise AI Application Development
Architect and build end-to-end AI applications designed for large-scale enterprise deployment.
Translate complex financial workflows into intelligent AI-driven systems with measurable business impact.
Own AI features from prototyping through production rollout.
LLM & Agent Systems
Customize, fine-tune, and deploy Large Language Models for regulated, real-world use cases.
Design intelligent agent systems capable of multi-step reasoning, orchestration, and decision support.
Implement Retrieval-Augmented Generation (RAG) pipelines to ensure accuracy, explainability, and contextual grounding.
AI Infrastructure & Integration
Build scalable AI services integrated with backend systems using Python-based APIs (FastAPI) and modern service architectures.
Collaborate with platform and backend teams to ensure security, performance, and maintainability.
Optimize inference pipelines for latency, cost, and throughput in production environments.
Workflow Orchestration & Frameworks
Develop and manage AI workflows using frameworks such as LangChain, LangGraph, or similar orchestration tools.
Design modular, extensible AI pipelines that can evolve with changing business and regulatory needs.
Performance, Reliability & Scale
Monitor and improve AI system performance, including model quality, reliability, and runtime efficiency.
Implement best practices for logging, evaluation, versioning, and continuous improvement of AI systems.
Who We’re Looking For
Core Experience
Hands-on experience building and deploying LLM-powered or agent-based AI applications.
Strong proficiency in Python and experience integrating AI systems with backend services.
Practical experience with RAG architectures, vector databases, and prompt or workflow optimization.
Experience taking AI solutions from proof-of-concept to production.
Technical Strengths
Deep familiarity with frameworks like LangChain, LangGraph, or equivalent tools.
Solid understanding of LLM deployment, inference optimization, and system-level tradeoffs.
Comfort working in enterprise-grade environments where reliability, security, and scale matter.
Nice to Have
Prior experience in BFSI, fintech, or other regulated industries.
Exposure to cost optimization strategies for large-scale model inference.
Experience collaborating closely with product managers and domain experts.
Why This Role
Work on AI systems already live with top-tier financial institutions—not just experiments.
Build real-world AI products that must meet enterprise performance, security, and reliability standards.
Collaborate with seasoned AI and BFSI experts on high-impact use cases.
Opportunity to shape the future architecture of enterprise AI platforms.
Core Skills
Applied AI Engineering · LLM Systems · AI Agents · RAG Architectures · LangChain · LangGraph · Python · FastAPI · Enterprise AI Deployment · Workflow Orchestration
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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