AVP _ Technical Architect AI_ Innovation HUB

BE/B Tech/MCA in Computer Science, Information Technology, Data Science, or similar engineering disciplines.

Overall, 8 12 years of experience in software engineering, with at least 3+ years architecting and scaling production AI solutions as a Tech lead or Tech Architect role.

Prior experience setting up an Innovation lab, CoE or greenfield technology unit inside a bank, exchange, depository or large NBFC.

5+ year of experience Hands-on proven expertise in designing and building highly distributed systems, microservices architectures, event-driven systems, and scalable APIs.

3+ year of experience Proven experience architecting on cloud platforms utilizing modern infrastructure-as-code (IaC) tools like Terraform and container orchestration via Kubernetes.

3+ years of experience in AI Platform engineering, distributed systems, or AI(GPU) infrastructure set up on Premises and on Sovereign Cloud.

Core Competencies (Must have):

1. Strong technical design and architecture credentials on on-premise and Cloud Infra (VPC design, IAM, KMS, networking, cost governance) with the ability to design cloud-native systems that remain portable to on-premise Linux (RHEL) environments container-first, no hard dependency on managed services.

2. Hands-on software engineering and clean coding mindset

3. Hands-on with containerisation and orchestration (Docker/Podman, Compose, Kubernetes) and Infrastructure as Code (Terraform).

4. Practical depth in modern AI systems LLM application architecture, RAG, agentic orchestration, model gateways, prompt and context engineering, evaluation and guardrail frameworks.

5. Working knowledge of classical ML for anomaly detection, forecasting and pattern recognition (e.g. isolation forests, gradient boosting, time-series methods).

6. Hands-on coding ability in Python (FastAPI or equivalent) and comfort with modern web front-ends (React/TypeScript).

7. Expertise AI and Agentic AI frameworks and orchestration tools like PyTorch, TensorFlow, LangChain, Hugging Face and Agentic AI platforms.

8. Data architecture: relational and time-series stores

(PostgreSQL/TimescaleDB), streaming and event pipelines (Kafka), analytical stores, graph and vector databases.

9. Demonstrated use of AI-assisted and spec-driven development workflows in real delivery not experimentation.

10. Experience architecting in a regulated environment with security, audit and data residency constraints.

11. Experience on designing Data Schemas and handling application with structured and unstructured data