Key Responsibilities:
- AI Architecture Engineering
- Define and own AI reference architectures for generative AI agentic systems and AI augmented applications
- Architect scalable solutions using LLMs multi agent systems orchestration frameworks and AI pipelines
- Design AI platforms supporting model serving prompt management RAG and workflow orchestration
- Establish architectural standards for performance scalability reliability and cost efficiency
- Platform Engineering Integration
- Build reusable AI components for LLM integration vector search embeddings and inference services
- Enable secure and scalable deployment using Kubernetes serverless platforms and CI CD pipelines
- Integrate AI capabilities into enterprise systems using APIs SDKs and event driven architectures
- Collaborate with QE teams to embed AI into test automation test data generation and intelligent validation
- Engineering Governance Quality
- Define architectural guardrails for model lifecycle versioning monitoring and rollback
- Ensure adherence to non functional requirements including performance observability and fault tolerance
- Leverage observability tools to monitor model performance and drift
- Review designs and implementations for architectural compliance and code quality
- Mentor engineers and architects on AI engineering best practices
- Core Platforms Frameworks Tooling
- LLM and foundation model platforms e
- g
- AWS Bedrock Azure OpenAI Vertex AI
- Agentic AI and orchestration frameworks LangChain LangGraph CrewAI AutoGen Google ADK or equivalent
- Vector databases and search technologies OpenSearch Pinecone FAISS Weaviate
- Model lifecycle and deployment tooling Kubernetes containers serverless runtimes
- CI CD and MLOps tooling for AI pipelines GitHub Actions Azure DevOps Jenkins
- Observability and monitoring tooling for AI systems OpenTelemetry Prometheus Grafana
- Client Orientation Leadership
- Partner with product and engineering teams to identify AI opportunities and shape roadmaps
- Support client workshops RFPs and solution presentations
- Mentor engineers on AI ML Gen AI best practices and emerging technologies
- Translate complex AI concepts into business friendly narratives
Technical Requirements:
- 13 years of experience in software engineering with 3 years in AI with strong architecture ownership
- Proven experience designing and implementing enterprise scale AI engineering or MLOps platforms
- Strong hands on experience with LLMs prompt engineering RAG and agent frameworks
- Proficiency in Python AI frameworks and cloud native AI services
- Experience in Kubernetes CI CD and secure deployment of AI models
- Experience integrating AI capabilities into enterprise scale systems
- Good to Have Skills
- Experience with multi agent orchestration and autonomous workflows
- Knowledge of model observability and monitoring tooling
- Exposure to QE platforms test automation frameworks or AI assisted testing
- Domain experience in regulated industries such as BFSI Healthcare Telecom
- Cloud and AI certifications
Preferred Skills:
Technology->Agile Testing->Agile Testing - ALL,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design,Technology->AI-AI Engineering->Databricks AI Engineering Services,Technology->AI-AI Engineering->LLMOps,Technology->AI-AI Engineering->MLOps,Technology->AI-AI Engineering->Model Optimization,Technology->AI-AI Engineering->Model Support,Technology->AI-Generative AI->Conversational AI Platform,Technology->AI-Generative AI->Generative AI - Basic->chains,Technology->AI-Generative AI->Generative AI for Data Analytics,Technology->AI-Generative AI->Prompt Engineering,Technology->Architecture->Architecture - ALL,Technology->Enterprise Architecture->Digital Architecture
