Technical Architect

Job Description

Job Summary
We are looking for a GenAI Architect to design, build, and guide the implementation of Generative AI solutions across enterprise use cases. The role requires strong hands-on and architectural expertise in LLMs, RAG, vector databases, and agentic AI frameworks, along with experience in cloud-native deployments and CI/CD automation. You will collaborate with business stakeholders, data/ML teams, and engineering teams to deliver scalable, secure, and production-ready GenAI platforms and applications.

Key Responsibilities:
. Architect and design end-to-end GenAI solutions including RAG pipelines, agentic workflows, and multimodal use cases.
. Translate business problems into GenAI-driven use cases, solution blueprints, and implementation roadmaps.
. Design and implement retrieval systems using embeddings, chunking strategies, metadata filters, reranking, and evaluation metrics.
. Select and integrate vector databases and optimize indexing, retrieval performance, and relevance tuning.
. Build agentic systems using frameworks such as LangChain, LangGraph, and related orchestration tools.
. Define cloud architecture patterns for scalable, secure, and reliable GenAI deployments.
. Drive productionization using CI/CD pipelines, containerization using best practices.
. Ensure responsible AI practices including security, governance, privacy, compliance, and monitoring.
. Provide technical leadership, reviews, mentoring, and best-practice guidance to engineering teams.
. Collaborate with product and delivery teams to ensure solution alignment with timelines and business outcomes.

Primary Skills (Must Have):
GenAI / LLM Architecture & Algorithms
. Strong knowledge of GenAI algorithms and LLM concepts: prompting, fine-tuning vs RAG, embeddings, context windows, token limits, hallucination control.
. Experience designing enterprise GenAI use cases (document Q&A, copilots, summarization, search, workflow automation, customer support, knowledge assistants).
. Understanding of evaluation techniques: groundedness, relevance, faithfulness, latency/cost trade-offs.
Vector Databases & Retrieval Systems
. Hands-on understanding of vector databases and similarity search concepts:
o embeddings, indexing, ANN search, hybrid search, metadata filtering
. Experience with tools like Pinecone, FAISS, Weaviate, Chroma, Milvus, Azure AI Search / Elastic (vector) (any relevant combination).
Agentic AI Frameworks
. Strong working knowledge of agentic frameworks such as:
o LangChain
o LangGraph
o MCP
o Tool calling / function calling, memory, planning, multi-agent workflows, guardrails
Cloud Architecture & Deployments
. Experience with cloud services and architecture for GenAI workloads:
o compute, networking, storage, IAM/security, logging/monitoring
. Knowledge of cloud components supporting AI/ML solutions (managed services preferred).
CI/CD and Productionization
. Experience implementing CI/CD pipelines for GenAI apps and services.
. Strong understanding of deployment patterns:
o containers (Docker), orchestration (Kubernetes), API deployment, model endpoint integration
. Familiarity with DevOps/MLOps practices: testing, observability, rollback, scaling, cost controls.

Mandatory Competencies