AI/ML Architect

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Job Description

About the Role

GenAI Architect

Responsibilities

Architect and design scalable, production-grade RAG pipelines, including:
Retriever selection and configuration.
Semantic search optimization.
Extractor logic and integration.
Comparative evaluation of vector databases (e.g., FAISS, Weaviate, Pinecone) vs. traditional search solutions.
Define and implement LLM-based agentic workflows, including:
Tool integration.
Memory handling (episodic, long-term, vector-based).
Dynamic planning and decision making within multi-agent systems.
Lead the design of LLM-based enterprise applications from data ingestion and fine-tuning to prompt engineering and output evaluation.
Collaborate closely with product managers, ML engineers, and developers to translate business needs into robust AI-powered systems.
Conduct performance benchmarking, cost optimization, and system design trade-offs for GenAI solutions.
Stay updated with the latest research and trends in LLMs, RAG, and agentic reasoning.

Required Skills

Strong knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) including:
End-to-end design of retriever-reader pipelines.
Knowledge of embedding models (OpenAI, Cohere, HuggingFace, etc.).
Trade-offs between vector stores and semantic indexing strategies.
Solid experience with LLM agent frameworks (LangChain, Semantic Kernel, Haystack, etc.).
Deep understanding of Tools, Memory, and Planning in agent-based systems.
Strong grasp of vector databases and their internal architectures (e.g., ANN algorithms, HNSW, IVF).
Ability to justify vector DB usage over traditional RDBMS with technical and performance reasoning.
Proven experience designing complex AI/ML or GenAI architectures at scale.
Proficiency with Python and key AI libraries: LangChain, Transformers, LlamaIndex, etc.
Experience working with commercial and open-source LLMs (GPT-4, Mistral, LLaMA, Claude, etc.).

Desirable Skills

Experience with fine-tuning LLMs or working with proprietary/custom models.
Exposure to prompt engineering for diverse tasks (summarization, QA, classification, etc.).
Understanding of MLOps and deployment strategies for LLM pipelines.

Education Qualification

This is not a developer role. Candidates with only application-layer experience or limited understanding of LLM internals, vector DB architecture, or agentic workflows will not be a fit. We expect this architect to drive architectural decisions, not just write code.