AI Architect

AI Architect
Exp: 12+ years

Location:

Chennai
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- Role

Summary:

We are looking for an experienced AI Architect to lead the design and implementation of enterprise-scale AI/GenAI solutions. This role bridges the gap between cutting-edge generative AI capabilities and robust engineering architectures, ensuring our AI systems are scalable, secure, and production-ready.

Key Responsibilities:

GenAI Architecture & Strategy
- Define and own the end-to-end architecture for Generative AI solutions including LLM-powered applications, RAG pipelines, agentic workflows, and multi-model orchestration
- Evaluate, select, and integrate foundation models (commercial and open-source) based on cost, latency, accuracy, and compliance requirements
- Design prompt engineering frameworks, guardrails, and evaluation strategies for GenAI systems
- Architect fine-tuning, distillation, and model customization pipelines where needed
- Stay current with the rapidly evolving GenAI landscape and translate research advancements into practical enterprise solutions.
Engineering Architecture
- Design scalable, resilient, and cost-efficient cloud-native architectures for AI/ML workloads
- Define reference architectures, design patterns, and technical standards for AI system development across the organization
- Architect data pipelines, feature stores, vector databases, and model serving infrastructure
- Ensure non-functional requirements — performance, security, observability, disaster recovery — are built into every solution
- Drive API design, microservices decomposition, and integration patterns for AI-enabled products.
Customer Engagement & Consulting
- Serve as the primary technical advisor for enterprise clients, leading discovery workshops, solution briefings, and architecture deep-dives.
- Translate client business challenges into well-scoped AI/GenAI solution architectures, producing proposals, statements of work, and technical roadmaps.
- Drive pre-sales and proof-of-concept engagements, demonstrating solution value and accelerating client decision-making.
- Gather and synthesize customer feedback to influence product direction and shape reusable consulting accelerators.
- Represent the organization at client executive briefings, industry conferences, and external AI forums.
Leadership & Governance
- Collaborate with product, engineering, data science, and platform teams to align AI architecture with business goals.
- Establish AI governance frameworks covering model lifecycle management, bias/fairness monitoring, and responsible AI practices.
- Conduct architecture reviews, provide technical mentorship, and build internal capability in AI/GenAI.
- Create and maintain architecture decision records (ADRs), technical documentation, and roadmaps.
- Represent the organization in vendor evaluations, technology partnerships, and industry forums.

Required Qualifications

- Experience:

12+ years in software/data engineering with at least 4+ years in AI/ML architecture roles.
- GenAI Expertise:
Hands-on experience building production systems with LLMs (GPT, Claude, Gemini, Llama, Mistral, etc.), RAG architectures, vector stores (Pinecone, Weaviate, pgvector),
and agentic frameworks (Lang Chain, Lang Graph, CrewAI, or similar)
- Engineering Architecture:
Deep knowledge of distributed systems, cloud platforms (AWS/Azure/GCP),
containerization (Kubernetes, Docker), and CI/CD for ML (MLOps/LLMOps)
- Data & ML Platforms:

Experience with ML platforms (Sage Maker, Vertex AI, Databricks), data
orchestration (Airflow, Dagster), and streaming systems (Kafka, Flink)
- Programming:
Strong proficiency in Python; working knowledge of Java, Go, or Type Script is a plus.
- System Design:
Proven ability to design systems that handle scale, fault tolerance, and low-latency inference.
- Communication:
Ability to articulate complex technical concepts to both engineering teams and executive stakeholders

Preferred Qualifications:

- Experience with multi-modal AI systems (text, image, audio, video)
- Familiarity with AI safety, alignment, and responsible AI frameworks
- Exposure to edge/on-device AI deployment
- Contributions to open-source AI projects or published research
- Experience in regulated industries (finance, healthcare, government) with compliance-aware AI deployments.

Tech Stack (Indicative)
Area Technologies:
Foundation Models/;
OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral
GenAI Tooling:
Lang Chain, Lang Graph, Llama Index, Semantic Kernel, Haystack
Vector Databases:
Pinecone, Weaviate, Qdrant, pgvector, ChromaDB
ML/MLOps:
Sage Maker, Vertex AI, MLflow, Kubeflow, Weights & Biases
Cloud & Infra: AWS/Azure/GCP, Kubernetes, Terraform, Docker
Data:
Spark, Kafka, Airflow, Snowflake, Delta Lake
Observability:
Datadog, Grafana, Lang Smith, Arize AI

What You'll Influence:
- The GenAI strategy and technical direction of the organization.
- Build vs. buy decisions for AI capabilities.
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