Lead AI Engineer – Agentic Systems (Lead exp. is mandatory)

TalentQuell; Bengaluru; Sur place; INR 2 500 000 - 5 000 000; Plein temps

Résumé du poste

TalentQuell is seeking an experienced software/ML engineer focused on production-grade LLM/agentic AI systems in Bengaluru. Candidates should demonstrate hands-on work with multiple agentic frameworks, direct SDK experience with Claude/OpenAI/Vertex AI, and strong Python, cloud, and observability skills.

Knowledge of pharma data and regulated data handling is a plus. The role emphasizes building scalable AI agents, ensuring performance, reliability, and compliance in production environments

Qualifications

• 5-7 years in software or ML engineering.
• 2+ years building and shipping production LLM or agentic AI systems.
• Hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI).
• Direct SDK experience with Claude, OpenAI, or Vertex AI.
• Python mastery including production-quality code, tests, packaging and profiling.
• Cloud deployment with AWS, Azure, or GCP and containerization basics.
• Agent observability with LangSmith/Helicone and similar tools.
• Experience with pharma commercial data (Rx/claims, brand performance).
• Regulated data handling experience (GxP, 21 CFR Part 11, HIPAA).

Connaissances

Software/ML engineering

Production LLM/agentic AI

Agentic frameworks

Python mastery

Cloud deployment

Observability

Regulated data handling

Debugged framework internals

Outils

Anthropic Claude API

OpenAI Assistants API

Vertex AI Agent Builder

LangSmith

Helicone

Terraform

CDK

Docker

Kubernetes

Description du poste

• 5-7 years in software or ML engineering; 2+ years building and shipping production LLM or agentic AI systems.
• Hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI); you have debugged framework internals, not just followed tutorials.
• Direct SDK experience: Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder.
• Python mastery: production-quality code, type annotations, unit and integration tests, packaging, and performance profiling.
• Cloud deployment: AWS, Azure, or GCP. Docker, Kubernetes, IaC basics (Terraform or CDK), CI/CD pipelines.
• Agent observability: LangSmith, Helicone, or equivalent - you have diagnosed latency, cost, and quality issues in production traces.
• Working knowledge of pharma commercial data: Rx/claims, NPI-level analytics, brand performance metrics.
• Experience operating in regulated data environments: GxP, 21 CFR Part 11, HIPAA- compliant data handling.