AI Architect

Futurea4; Hyderabad, Pune District, Chennai District; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Futurea4 is seeking an experienced AI architect to define and implement enterprise GenAI and LLM architectures. You will lead POCs, design end-to-end AI solutions, and ensure security, governance, scalability and cost efficiency across multi-cloud platforms.

You will mentor teams across AI, data science and engineering, translate business requirements into scalable architecture, and drive technology decisions in a fast-paced, results-driven environment.

Qualifications

• Experience designing enterprise GenAI and LLM architectures.
• Hands-on with RAG, embeddings and vector databases.
• Experience with LLM fine-tuning, guardrails and hallucination mitigation.
• Proficient in Python and ML pipelines.
• Knowledge of multi-cloud or cloud-native architectures.

Responsibilities

• Define future-state AI/GenAI architecture and technology roadmap.
• Review product architecture for AI-driven transformation.
• Design enterprise-grade LLM, RAG and Agentic AI solutions.
• Lead technical POCs to validate architectures and AI technologies.
• Define architecture standards and reusable frameworks.
• Collaborate with Data Engineering, Data Science, Product and Engineering to drive implementation.
• Ensure AI solutions meet security, governance, scalability and cost requirements.
• Provide technical leadership through architecture decisions.

Skills

GenAI/LLM architecture

RAG embeddings

Python

Cloud & multi-cloud

Databricks/Snowflake

MLOps/LLMOps

Vector databases

Docker/Kubernetes

Tools

Databricks

Snowflake

Docker

Kubernetes

FAISS

Pinecone

Weaviate

Job description

Mandatory Skills & Expected Experience1. GenAI & LLM Architecture

• Strong hands-on experience designingenterprise GenAI and LLM architectures.
• Strong expertise inRAG, embeddings, vector databases, prompt engineering and AI agents.
• Experience withLLM fine-tuning, guardrails and hallucination mitigation.
• Experience with LLMs such asOpenAI, LLaMA, Gemini or equivalent.
• Ability to design scalable GenAI solutions fromPOC to production.

2. AI/ML & Data Engineering

• Strong understanding ofMachine Learning algorithms, model evaluation and experimentation.
• Hands-on experience withfeature engineering and ML pipelines.
• Strong understanding of structured and unstructured data processing.
• Experience withPython and PySpark.
• Knowledge ofTensorFlow, PyTorch or Scikit-learn.
• Understanding of model explainability, bias/fairness and responsible AI.

3. AI Platform & Enterprise Architecture

• Proven experience designingend-to-end enterprise AI architectures.
• Strong understanding of integration patterns, APIs, microservices and distributed systems.
• Experience architectingbatch and real-time AI inference solutions.
• Strong understanding of scalability, security, governance and resilience.
• Experience withmulti-cloud or cloud-native architectureis expected.

4. Databricks / Snowflake

Strong hands-on experience withat least oneof:

• Databricks / Databricks Mosaic AI
• Snowflake / Snowflake Cortex

Experience should include relevant areas such as:

• Data engineering and transformation
• ML/AI pipelines
• Lakehouse architecture
• AI/ML platform integration
• Data governance and security
• Performance and cost optimization

5. RAG & Agentic AI

• Strong experience designingenterprise RAG architectures.
• Expertise inchunking, embeddings, retrieval, reranking and grounding.
• Hands-on experience withVector Databasessuch as FAISS, Pinecone, Weaviate or equivalent.
• Experience designingAI agents, tool calling, function calling and workflow orchestration.
• Exposure tomulti-agent architecturesis preferred.

6. Cloud & AI Platform Architecture

Experience architecting AI solutions on one or more:

• AWS
• Microsoft Azure
• Google Cloud Platform
• Azure OpenAI / Azure AI Foundry
• AWS Bedrock
• Google Vertex AI

Strong understanding of cloud-native architecture, security and scalability is expected.

7. MLOps / LLMOps

• Strong understanding ofMLOps and LLMOps lifecycle management.
• Experience withMLflow, Kubeflow, Azure ML or equivalent.
• Experience with CI/CD, model/version management and deployment.
• Experience with monitoring, evaluation, rollback and lifecycle management.
• Hands-on exposure toDocker and Kubernetes.

8. Observability, Performance & Cost Optimization

• Experience implementingAI/LLM observability and evaluation frameworks.
• Understanding of tracing, model quality monitoring and feedback loops.
• Experience optimizingLLM latency, throughput and token consumption.
• Knowledge of model routing, caching and cost governance.
• Ability to balancequality, performance, scalability and cost.

9. Security, Governance & Responsible AI

• Experience designing secure enterprise AI solutions.
• Strong understanding ofAI governance, data privacy, model risk and compliance.
• Experience implementing guardrails, access controls and responsible AI practices.
• Understanding of explainability, auditability and model monitoring.

10. Architecture Leadership & Communication

• Ability to conductarchitecture reviews and technical assessments.
• Experience mentoring AI, Data Science and Engineering teams.
• Strong stakeholder management and executive communication skills.
• Ability to translate business requirements into scalable AI architecture.
• Experience driving technology decisions and influencing enterprise AI strategy.

Key Responsibilities

• Definefuture-state AI/GenAI architecture and technology roadmap.
• Review existing product architecture and identify opportunities for AI-driven transformation.
• Design enterprise-gradeLLM, RAG and Agentic AI solutions.
• Lead technical POCs to validate architecture and emerging AI technologies.
• Define architecture standards, reusable frameworks and engineering best practices.
• Work with Data Engineering, Data Science, Product and Engineering teams to drive implementation.
• Ensure AI solutions meet requirements aroundsecurity, governance, scalability, performance and cost.
• Provide technical leadership and guide teams through architecture and implementation decisions.

Preferred Candidate Profile

• 12+ yearsof overall technology experience with significant experience inAI/ML, Data or Architecture.
• Strong recent experience inGenAI/LLM architecture and enterprise AI solutions.
• Strong combination ofGenAI + RAG + Agentic AI + Python + Cloud + Databricks/Snowflake.
• Proven experience taking AI solutions fromPOC architecture production.
• Strong enterprise architecture and stakeholder management experience.
• Candidates with experience inDatabricks Mosaic AI, Snowflake Cortex, Azure AI Foundry, AWS Bedrock or Vertex AIwill be preferred.

Core Mandatory StackGenAI / LLMs | RAG | AI Agents | Vector DB | Python | AI/ML Architecture | Databricks / Snowflake | Cloud AI | MLOps / LLMOps | Enterprise ArchitecturePreferred:Azure OpenAI / AI Foundry | AWS Bedrock | Vertex AI | Databricks Mosaic AI | Snowflake Cortex | MLflow | Docker/Kubernetes | LangSmith/LangFuseRole Fitment ImportantThis is anArchitecture + Hands-on AI role. Candidates should have demonstrable experience indesigning and implementing AI/GenAI solutions, rather than profiles focused only on people management, strategy or high-level architecture.Hands-on GenAI + Enterprise Architecture + RAG/Agents + Databricks/Snowflake + Cloud experience is mandatory.