Gen AI Architect

Job Title:

Gen AI Architect

Experience:
8 – 12 Years

Work Mode:
Work from Office (WFO)

Locations:
Chennai | Bangalore | Hyderabad

Position Overview
We are looking for an experienced
GenAI Architect
to lead the design and implementation of cutting-edge Generative AI solutions that drive innovation, scalability, and enterprise transformation.

This role demands deep expertise in
LLM-based architectures
,
RAG (Retrieval-Augmented Generation)
systems,
agentic workflow orchestration
, and
MLOps deployment
. The ideal candidate will combine strong hands-on technical skills with architectural vision, ensuring secure, compliant, and high-performance GenAI ecosystems.

Key Responsibilities

Generative AI (GenAI) Architecture

• Architect and design
  enterprise-grade LLM-based systems
  optimized for performance, scalability, and reliability.
• Demonstrate strong understanding of
  vector embeddings
  , similarity search (cosine/IP/L2),
  chunking strategies
  , and
  reranking
  .
• Build and optimize
  RAG pipelines
  — including indexing, metadata, hybrid search, and evaluator frameworks.
• Apply
  prompt engineering
  for tool use, function calling, and multi-agent planning.
• Develop and orchestrate
  agentic workflows
  using frameworks such as
  LangGraph
  or similar; integrate tools/services via
  MCP-compatible patterns
  .
• Implement
  NL2SQL
  approaches, ensuring SQL safety through schema constraints, sandboxes, and secure API integrations.
• Evaluate and balance trade-offs between
  generic LLMs
  and
  fine-tuned models
  , optimizing for accuracy, latency, and operational cost.
• Embed
  security, compliance, and data governance
  principles within all AI workflows, including RBAC/ABAC, auditability, and privacy controls.

MLOps & Deployment

• Manage
  end-to-end deployment
  using frameworks such as
  MLflow
  ,
  Kubeflow
  ,
  SageMaker
  , or
  Vertex AI
  .
• Implement
  CI/CD pipelines
  for AI models, ensuring consistent delivery and robust version control.
• Utilize
  Docker
  and
  Kubernetes
  for model containerization, orchestration, and scalability.
• Establish
  monitoring, tracing, and observability
  for model performance, latency, and cost metrics.

Preferred Experience

• Hands-on experience with
  cloud platforms
  such as
  AWS
  ,
  Azure
  ,
  GCP
  , or
  OCI
  .
• Strong understanding of
  large-scale distributed systems
  and
  big-data frameworks
  (Spark, Hadoop).
• Expertise in
  retrieval optimization
  (hybrid lexical + vector search, metadata filtering, rerankers).
• Knowledge of
  model fine-tuning
  and adapter techniques (
  LoRA, SFT, DPO
  ) with evaluation best practices.
• Experience in
  Document AI
  ,
  layout parsing
  , and schema design for unstructured data.
• Familiarity with
  observability stacks
  for LLM applications — tracing, evaluation dashboards, and performance SLOs.
• Understanding of
  caching, batching
  , and
  KV-cache
  strategies for improved throughput and cost optimization.
• Exposure to
  secure tool-use patterns
  , constrained decoding, and policy-based control mechanisms.

Assessment Framework

• Portfolio Review:
  Evaluation of production-ready RAG or agentic systems, including design, architecture, and measurable outcomes.
• Technical Exercise:
  Design an intent router, justify model selection (base vs. fine-tuned), propose chunking/metadata strategies, and define evaluation metrics.
• Scenario Discussion:
  Identify and mitigate failure modes (hallucinations, tool errors, SQL risk).
• Governance Evaluation:
  Demonstrate understanding of access controls, PII handling, audit logging, and red-teaming practices.

Qualifications

• Bachelor's or Master's degree in
  Computer Science
  ,
  Artificial Intelligence
  ,
  Data Engineering
  , or a related discipline.
• 8–12 years of total experience, including at least 3+ years in
  AI/ML architecture
  or
  Gen AI system design
  .
• Proficiency in
  Python
  ,
  LLM/RAG frameworks
  , and modern
  containerization and CI/CD workflows
  .
• Strong communication, leadership, and problem-solving abilities with a focus on delivering business-impactful AI solutions.