AI/ML Engineer

Software Engineer - AI/ML (Multi-Agent Systems)

Location: Bengaluru, India

Experience: 2-4 years

Team: AI/ML Engineering, L&T Finance (LTFS)

About the Role

We’re building enterprise-grade multi-agent AI systems and fine-tuned models that power real production workflows. You’ll work across the stack: designing agent architectures, fine-tuning models for domain-specific tasks, and shipping systems that need to hold up at scale.

Scope & Ownership

• Own end-to-end delivery of a defined workstream - e.g., an agent module or a fine-tuning pipeline - from design through deployment and monitoring
• Make implementation-level architecture decisions independently; escalate cross-system or infra-level decisions
• Work with moderate supervision: given a problem statement and constraints, you drive the technical approach
• Mentor interns or fresher hires on your workstream
• Directly accountable for the reliability and performance of what you ship

What You’ll Do

• Design, build, and deploy multi-agent AI systems (agent orchestration, tool use, inter-agent communication, memory/state management)
• Fine-tune and evaluate LLMs/ML models for domain-specific use cases; manage the full lifecycle from data prep to deployment
• Contribute to system architecture decisions within your workstream - service boundaries, data flow, scalability, and integration patterns
• Write production-quality code in Python and/or Java
• Collaborate with cross-functional teams (product, infra, data) to take AI features from prototype to production
• Debug and optimize model performance, latency, and cost in live systems
• Participate in code reviews and contribute to engineering best practices

What We’re Looking For

• 2-4 years of professional software engineering experience
• Strong proficiency in Python and/or Java
• Hands-on experience building or working with multi-agent systems (e.g., LangGraph, AutoGen, CrewAI, or custom agent frameworks)
• Practical experience with model fine-tuning (LoRA/QLoRA, full fine-tuning, or similar techniques) and the ML training pipeline
• Solid understanding of system architecture - designing for scalability, reliability, and maintainability
• Familiarity with REST APIs, microservices, and cloud deployment (any major cloud provider)
• Demonstrated ability to own a project end-to-end with limited hand-holding
• Strong problem-solving skills and ability to work with some ambiguity

Nice to Have

• Experience with vector databases, RAG pipelines, or retrieval-augmented systems
• Exposure to MLOps tooling (experiment tracking, model versioning, CI/CD for ML)
• Prior experience in fintech, BFSI, or regulated enterprise environments
• Familiarity with Spring Boot (Java stack) or FastAPI/Django (Python stack)