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Senior AI Engineer - AI-Powered Communications

6 - 10 years

6 - 10 Lacs

Posted:22 hours ago| Platform: Foundit logo

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Work Mode

On-site

Job Type

Full Time

Job Description

What You ll Do

  • Design & Build: Develop mutli-agent AI systems for the UCaaS platform, focusing on NLP, speech recognition, audio intelligence and LLM powered interactions.
  • Rapid Experiments: Prototype with open-weight models (Mistral, LLaMA, Whisper, etc.) and scale what works.
  • Code for Excellence: Write robust code for AI/ML libraries and champion software best practices.
  • Optimize for Scale & Cost: Engineer scalable AI pipelines, focusing on latency, throughput, and cloud costs.
  • Innovate with LLMs: Fine-tune and deploy LLMs for summarization, sentiment and intent detection, RAG pipelines, multi-modal inputs and multi-agentic task automation.
  • Own the Stack: Lead multi-agentic environments from data to deployment and scale.
  • Collaborate & Lead: Integrate AI with cross-functional teams and mentor junior engineers.

What You Bring

  • Experience:6-10 yearsof professional experience, with a mandatory minimum of 2 years dedicated to a hands-on role in a real-world, production-level AI/ML project.
  • Coding & Design: Expert-level programming skills inPythonand proficiency in designing and building scalable, distributed systems.
  • ML/AI Expertise: Deep, hands-on experience with coreML/AI libraries and frameworks, Agentic Systems, RAG pipelines
  • Hands-on experience in usingVector DBs
  • LLM Proficiency: Proven experience working with and fine-tuning Large Language Models (LLMs).
  • Scalability & Optimization Mindset: Demonstrated experience in building and scaling AI services in the cloud, with a strong focus on performance tuning and cost optimization of agents specifically.

Nice to Have

  • Youve tried outagent frameworkslike LangGraph, CrewAI, or AutoGen and can explain the pros and cons of autonomous vs. orchestrated agents.
  • Experience with MLOps tools and platforms (e.g., Kubeflow, MLflow, Sagemaker).
  • Real-time streaming AI experience token-level generation, WebRTC integration, or live transcription systems
  • Contributions to open-source AI/ML projects or a strong public portfolio (GitHub, Kaggle).

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