AI Application Architect

This role will be responsible for designing AI & Machine Learning solutions on cloud-based platforms, explore emerging trends in AI, develop proof-of-concepts and engage with internal and external ecosystem to progress the proof of concepts to production.

Key Roles & Responsibilities:

• This role focuses on consuming, integrating, and operationalizing advanced AI models-from Large Language Models (LLMs) to Small Language Models (SLMs) - into secure, governed, and scalable business solutions.
• Governance by Design: enforce data-handling policies in code (prompt redaction Middleware, retrieval allow-lists, per-use-case policies).
• Prompt/Agent CI/CD: add evaluation gates (answer quality, safety) to pipelines; canary deploys with feature flags; automated rollback on drift.
• Model Lifecycle: manage SLM/LoRA fine-tuning with consented datasets, synthetic augmentation policies, and model registry entries w/lineage.
• Observability: implement tracing (eg, request - retrieved docs - model output - tool calls), latency & cost SLOs; alerts on hallucination/safety incidents.
• Provider Abstraction: wrap OpenAI/Gemini/Azure OpenAI/Vertex behind an interface; capture provider/region, model/version, and quota routing.

• Proficient in Python, PyTorch, TensorFlow, or similar frameworks
• Experience with supervised, unsupervised, and reinforcement learning
• NLP Expertise: Solid grounding in Natural Language Processing (NLP) concepts - tokenization, embeddings, semantic search, text classification, and summarization.
• Generative AI & LLMs: Strong understanding of Large Language Models (LLMs) and Generative AI (GAI), with hands-on experience in LangGraph, LangChain, LlamaIndex, OpenAI APIs and Model Context Protocol (MCP) for building AI agents and conversational systems, Transformers, and Prompt engineering
• Strong understanding of statistics, probability, and model evaluation techniques

Hands on Java, Cloud and Kubernetes principles,

• Must have experience in architecting & development of applications on cloud infrastructure using different AWS services (or other public cloud like IBM Cloud, Azure, Google Cloud).
• Experience with AWS Cloud paradigms like lambda, cloudfront, s3
• Experienced with different forms of architectures like SOA, Microservices, EDA.
• Responsive Web applications, Web Services and batch applications development
• Application development tools and frameworks like maven, ant, check style, PMD, fortify, junit, SONAR, SOAP UI, REST Assured etc.
• Hands on with Java/J2EE design patterns

Must-Have Skills

• Define and enforce AI data-handling policies (PII/PCI/GDPR) across prompts, retrieval, logs, and analytics. Implement redaction/masking, tenant isolation, model risk tiers, and provider due diligence. Own evaluation and approval workflows for prompt/model changes, with audit-ready lineage and retention controls.
• API Consumers: integrate ChatGPT (OpenAI) and Gemini via provider SDKs with fallback logic and request/response schemas.

Good-to-Have Skills

• Broader understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning).
• Exposure to multimodal AI (text + vision, speech).
• Data engineering & analysis skills: ETL pipelines, feature engineering, EDA.
• Familiarity with MLOps/DevOps (CI/CD pipelines, monitoring, retraining).
• Understanding of knowledge graphs, embeddings optimization, and enterprise search integration.
• Strong collaboration and communication skills to work with cross-functional teams and explain AI concepts to stakeholders.