AI Engineer

 Generative AI – production-grade GenAI solution design and deployment

Agentic AI / Multi-Agent Systems – agent orchestration, tool-using agents, memory-enabled systems

Advanced RAG Architecture – multi-stage retrieval, re-ranking, multi-hop retrieval and reasoning

Python (core), FastAPI, React

Vector Databases & Embedding Models – hybrid search architectures

LLM Integration – LLM APIs, multi-model AI architectures

AI Platform Engineering – model serving, feature stores, GPU/infra readiness

Production AI Deployment – observability, logging, tracing, reliability engineering, graceful degradation, circuit breakers

AI Evaluation Frameworks – A/B testing, benchmarking, telemetry-based optimization

Prompt Engineering – templates, versioning, testing methodologies

Observability & Monitoring – real-time dashboards, automated alerting, incident response

Enterprise/Cloud-Native Architecture – distributed systems at scale

Cloud AI Platforms – GCP/Azure

With loops & graphs hands on

Architect end-to-end AI systems including advanced RAG pipelines, multi-agent orchestration frameworks, and multi-model AI integrations built for modularity, scalability, and operational excellence.

Define enterprise standards for prompt engineering (templates, versioning, testing, evaluation) and performance optimization (model selection, caching, resource utilization, cost).

Lead deployment of AI solutions into production with comprehensive observability, reliability engineering, monitoring dashboards, automated alerting, and incident response — meeting stringent SLOs.

Design scalable data ingestion frameworks for structured, unstructured, and real-time streaming data, along with vector database architectures, hybrid search, preprocessing pipelines, and data quality/governance frameworks.

Establish quantitative AI evaluation frameworks (A/B testing, benchmarking, user feedback, telemetry) and drive continuous improvement across prompts, retrieval strategies, agent workflows, and model configurations.

Partner with platform and infrastructure teams on AI workload readiness (GPU infra, model serving, feature stores, storage, networking) and define enterprise AI platform requirements.

Ensure AI solutions adhere to enterprise governance and compliance; apply Responsible AI principles — fairness, transparency, accountability, and bias mitigation.