Assistant Manager

Job Summary

We are seeking a Assistant manager - AI Engineer with strong hands-on experience in designing, building, and deploying production-grade LLM and multi-agent systems. The ideal candidate will have expertise in architecting autonomous agents for generation, evaluation, orchestration, and workflow automation, with a strong focus on reliability, scalability, governance, and human-in-the-loop controls.

The role requires deep technical expertise in Python, Large Language Models (LLMs), Agentic AI frameworks, RAG, vector search, prompt engineering, LLM evaluation, and guardrails.

Key Responsibilities

• Design and implement scalable multi-agent architectures involving generation agents, evaluation/critic agents, orchestration agents, and autonomous workflow systems.
• Build and deploy LLM-powered applications and generation pipelines that deliver reliable, high-quality outputs at scale.
• Develop LLM-as-a-Judge, critic-agent, and automated evaluation frameworks to assess output quality, accuracy, compliance, and reliability.
• Design and implement effective prompt engineering, grounding, Retrieval-Augmented Generation (RAG), and vector search strategies using trusted enterprise data.
• Build robust agent orchestration workflows using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar technologies.
• Implement human-in-the-loop approval gates, feedback mechanisms, and continuous improvement loops to enhance agent performance and reliability.
• Design and implement AI guardrails, validation layers, content governance, and Responsible AI controls for production-grade systems.
• Integrate and optimize solutions using major LLM providers and APIs, selecting appropriate models based on quality, latency, scalability, and cost requirements.
• Deploy and manage LLM and agent-based applications on cloud AI/ML platforms such as Databricks Mosaic AI, Model Serving, or similar platforms.
• Establish LLMOps and MLOps practices, including experiment tracking, prompt/version management, model monitoring, tracing, evaluation, and observability.
• Optimize AI systems for performance, latency, token usage, infrastructure cost, and output quality.
• Collaborate with data engineering, ML engineering, platform, product, and business teams to translate business requirements into scalable Agentic AI solutions.

Required Skills

• Strong hands-on experience with Python and building LLM-powered applications in production environments.
• 6 to 10 years of experience in AI engineering, data science, analytics.
• Strong experience designing and implementing Agentic AI and multi-agent systems.
• Hands-on expertise with multi-agent and orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar frameworks.
• Strong knowledge of Prompt Engineering, Retrieval-Augmented Generation (RAG), grounding techniques, vector databases, and semantic search.
• Experience working with major LLM providers and APIs, with a strong understanding of model selection and trade-offs related to cost, latency, performance, and quality.
• Hands-on experience with LLM evaluation frameworks, including LLM-as-a-Judge, critic agents, automated evaluation, and quality assessment techniques.
• Strong understanding of AI guardrails, validation frameworks, Responsible AI, content governance, and safety controls.
• Experience implementing human-in-the-loop workflows and feedback mechanisms for AI systems.
• Experience deploying LLMs and Agentic AI applications on cloud ML/AI platforms such as Databricks Mosaic AI, Model Serving, or equivalent platforms.
• Knowledge of MLOps / LLMOps practices, including experiment tracking, prompt management, version control, monitoring, tracing, and observability.
• Strong understanding of AI application performance optimization, including latency, scalability, token consumption, and cost optimization.
• Experience taking AI/LLM applications from prototype or proof-of-concept stages to production deployment.
• Strong problem-solving skills and the ability to work collaboratively with cross-functional engineering and business teams.

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