We are seeking a visionary AI Architect to lead the design and integration of cutting-edge AI systems, including Generative AI, Large Language Models (LLMs), multi-agent orchestration, and retrieval-augmented generation (RAG) frameworks.
This role demands a strong technical foundation in machine learning, deep learning, and AI infrastructure, along with hands-on experience in building scalable, production-grade AI systems on the cloud.
The ideal candidate combines architectural leadership with hands-on proficiency in modern AI frameworks, and can translate complex business goals into innovative, AI-driven technical solutions.
Primary Stack & Tools :
- Languages : Python, SQL, Bash.
- ML / AI Frameworks : PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers.
- GenAI & LLM Tooling : OpenAI APIs, LangChain, LlamaIndex, Cohere, Claude, Azure OpenAI.
- Agentic & Multi-Agent Frameworks : LangGraph, CrewAI, Agno, AutoGen.
- Search & Retrieval : FAISS, Pinecone, Weaviate, Elasticsearch.
- Cloud Platforms : AWS, GCP, Azure (preferred : Vertex AI, SageMaker, Bedrock).
- MLOps & DevOps : MLflow, Kubeflow, Docker, Kubernetes, CI / CD pipelines, Terraform, FAST API.
- Data Tools : Snowflake, BigQuery, Spark, Airflow.
Key Responsibilities :
Architect scalable and secure AI systems leveraging LLMs, GenAI, and multi-agent frameworks to support diverse enterprise use cases (e.g., automation, personalization, intelligent search).Design and oversee implementation of retrieval-augmented generation (RAG) pipelines integrating vector databases, LLMs, and proprietary knowledge bases.Build robust agentic workflows using tools like LangGraph, CrewAI, or Agno, enabling autonomous task execution, planning, memory, and tool use.Collaborate with product, engineering, and data teams to translate business requirements into architectural blueprints and technical roadmaps.Define and enforce AI / ML infrastructure best practices, including security, scalability, observability, and model governance.Manage technical road-map, sprint cadence, and 35 AI engineers; coach on best practices.Lead AI solution design reviews and ensure alignment with compliance, ethics, and responsible AI standards.Evaluate emerging GenAI & agentic tools; run proofs-of-concept and guide build-vs-buy decisions.Qualifications :
10+ years of experience in AI / ML engineering or data science, with 3+ years in AI architecture or system design.Proven experience designing and deploying LLM-based solutions at scale, including fine-tuning, prompt engineering, and RAG-based systems.Strong understanding of agentic AI design principles, multi-agent orchestration, and tool-augmented LLMs.Proficiency with cloud-native ML / AI services and infrastructure design across AWS, GCP, or Azure.Deep expertise in model lifecycle management, MLOps, and deployment workflows (batch, real-time, streaming).Familiarity with data governance, AI ethics, and security considerations in production-grade systems.Excellent communication and leadership skills, with the ability to influence technical and business stakeholders.(ref : hirist.tech)
