Infosys
Website:
infosys.com
Company:
https://www.linkedin.com/company/infosys
Seniority: Not Applicable
Industries: IT Services and IT Consulting
Job details:
- 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
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.
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