Senior LLM Engineer
Description
About the Role:
The individual will lead the design, development, and deployment of complex, autonomous agentic AI solutions within production environments, working across multi-agent frameworks and orchestrating large language models. The position is for scalable applied AI, with a focus on delivering robust and fair agentic systems that deliver organizational value, autonomy, and operational efficiency.
Responsibilities:
Architect and deploy scalable multi-agent AI systems with advanced tool integration.
Build and optimize AI pipelines using frameworks like LangGraph, ADK, and LangChain.
Integrate agentic AI into enterprise workflows to automate complex business processes.
Review and execute code written by AI agents to ensure correctness and maintainability.
Review agent configurations, prompts, and tool wrappers to prevent behavioral or performance regressions.
Audit agent-generated ML workflows to catch flaws like target leakage.
Launch, monitor, and debug distributed training jobs and auto-repair loops.
Design evaluation suites and end-to-end scenarios for non-deterministic agents.
Debug agent workflows and align systems with regulatory and ethical standards.
Ensure security, transparency, fairness, and reliability across AI systems.
Mentor engineers and promote modern LLM practices across teams.
Requirements
4+ years of industry experience, including 1+ years with agentic orchestration frameworks (LangChain, LangGraph, ADK).
Proficiency in multi-agent framework design, tool calling, API orchestration, and vector databases/RAG.
Experience with continuous monitoring, telemetry, and observability tooling for non-deterministic AI agents.
Strong background in LLM evaluation methodologies (code-based, LLM-as-a-Judge) and end-to-end testing.
Strong Python skills (type hints, absl, absltest) and senior-level code/prompt reviewing capabilities.
Practical knowledge of the ML lifecycle (data preprocessing, feature engineering, model design, JAX/TensorFlow).
Experience launching and debugging distributed training jobs across cloud platforms (AWS, GCP, Azure).
Knowledge of MLOps practices, data privacy, model security, and AI compliance regulations.
On-device ML (TFLite, Edge TPU, Gemini Nano, latency and power profiling).
Experience with Hugging Face, Neo4j, or knowledge graphs.
Background in autonomous decision-making systems, anomaly detection, and dynamic process optimization.
Track record of publishing, presenting, or open-sourcing agentic AI innovations.
Strong problem-solving aptitude, collaborative mindset, and excellent communication skills.
Location: Ahmedabad/Pune