Questhiring
Website:
questhiring.com
Company:
https://www.linkedin.com/company/questhiring
Seniority: Mid-Senior level
Industries: Software Development
Job details:
Engineering Manager – AI / Agentic AI
Location: Gurgaon
Experience: 10–14 Years
Employment Type: Full-Time
Budget : 75-80 LPA
About the Role
We are looking for an experienced Engineering Manager – AI / Agentic AI to lead the design, development, and deployment of next-generation AI-powered products and intelligent agent systems.
The ideal candidate will combine strong engineering leadership with hands-on expertise in Generative AI, LLMs, RAG, LangChain, LangGraph, AI Agents, and scalable AI architectures.
You will lead a team of engineers and AI specialists to build production-grade Agentic AI solutions that can reason, plan, use tools, interact with enterprise systems, and autonomously execute complex workflows.
This is a highly impactful role for someone who has moved beyond experimentation and has experience taking GenAI/Agentic AI solutions from PoC to production at scale.
Key Responsibilities
Engineering & AI Leadership
- Lead and mentor a team of AI/ML Engineers, Software Engineers, and AI specialists.
- Define the technical roadmap and architecture for GenAI and Agentic AI platforms.
- Drive engineering best practices across design, development, testing, deployment, monitoring, and continuous improvement.
- Translate business problems into scalable AI-powered solutions.
- Build a strong engineering culture focused on innovation, quality, reliability, and speed.
Agentic AI & LLM Engineering
- Architect and build AI Agents and multi-agent systems capable of reasoning, planning, decision-making, tool usage, and task execution.
- Design agent workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI or equivalent frameworks.
- Build systems involving function/tool calling, agent memory, planning, orchestration, routing, and autonomous workflows.
- Evaluate and select appropriate LLMs and AI models based on use case, latency, cost, accuracy, and scalability.
- Work with models from OpenAI, Anthropic, Google, Meta, open-source LLMs, or equivalent platforms.
RAG & Knowledge Systems
- Design and implement production-grade Retrieval-Augmented Generation (RAG) architectures.
- Work with vector databases, embeddings, semantic search, hybrid search, reranking, chunking, indexing, and retrieval strategies.
- Build enterprise knowledge systems that combine structured and unstructured data.
- Optimize RAG pipelines for accuracy, relevance, latency, scalability, and cost.
- Experience with technologies such as Pinecone, Weaviate, Milvus, FAISS, Elasticsearch/OpenSearch, pgvector or equivalent.
AI Platform & Production Engineering
- Drive the development of scalable APIs, services, and infrastructure supporting AI applications.
- Build production-grade AI systems with strong focus on availability, observability, security, scalability, and performance.
- Establish LLMOps / MLOps practices for model deployment, monitoring, evaluation, and lifecycle management.
- Implement AI observability and monitoring covering latency, token consumption, cost, hallucination, quality, and model performance.
- Establish robust CI/CD and automated testing for AI applications.
AI Evaluation & Quality
- Define frameworks for evaluating LLM and Agentic AI applications.
- Establish evaluation strategies for accuracy, groundedness, relevance, hallucination, toxicity, safety, and task completion.
- Experience with AI evaluation frameworks/tools such as DeepEval, RAGAS, LangSmith, Arize Phoenix or equivalent.
- Build feedback loops and continuously improve AI systems based on production performance.
Stakeholder & Product Collaboration
- Partner closely with Product, Data Science, Architecture, and Business teams to identify high-value AI opportunities.
- Translate product requirements into technical architecture and execution plans.
- Communicate complex AI concepts effectively to senior leadership and non-technical stakeholders.
- Drive multiple AI initiatives from ideation → PoC → MVP → production → scale.
Must-Have Skills
- 10–14 years of overall software/engineering experience.
- Proven experience in engineering leadership / people management.
- Strong hands-on experience building Generative AI / LLM applications.
- Strong expertise in RAG architecture and implementation.
- Hands-on experience with LangChain and/or LangGraph.
- Strong understanding of Agentic AI architectures and AI Agents.
- Experience with LLM orchestration, prompt engineering, tool/function calling, agent memory and workflows.
- Strong programming skills in Python.
- Strong experience with REST APIs, microservices, distributed systems, and cloud-native architectures.
- Experience working with vector databases and embedding models.
- Experience taking AI solutions from PoC to production.
- Strong understanding of LLM evaluation, observability, and production monitoring.
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Strong understanding of Docker, Kubernetes, CI/CD, and scalable cloud infrastructure.
Good to Have
- Experience building multi-agent systems.
- Experience with LangGraph, AutoGen, CrewAI, Semantic Kernel or similar frameworks.
- Experience with MCP (Model Context Protocol) and tool-based AI architectures.
- Experience with DeepEval, RAGAS, LangSmith, Arize Phoenix or equivalent evaluation/observability tools.
- Experience with fine-tuning, LoRA/PEFT, model optimization, or open-source LLMs.
- Knowledge of transformers, Hugging Face, PyTorch or TensorFlow.
- Experience with AI security, guardrails, prompt injection prevention, data privacy, and responsible AI.
- Experience building AI applications involving real-time decisioning or high-volume enterprise workflows.
- Experience with event-driven architectures and streaming platforms such as Kafka.
- Exposure to GenAI product development and AI-first engineering organizations.
What We Are Looking For
The ideal candidate is someone who:
- Has strong engineering fundamentals along with deep GenAI expertise.
- Has actually built and deployed Agentic AI systems, rather than only working on AI PoCs.
- Understands how to design reliable, scalable and cost-efficient LLM applications.
- Can balance hands-on technical contribution with engineering leadership.
- Has experience managing and mentoring strong engineering teams.
- Is passionate about the rapidly evolving Agentic AI ecosystem and actively experiments with emerging frameworks, models, and architectures.
Click on Apply to know more.
