Senior Software Engineer – AgentOps at Pearson PlcWestminster

Bangalore, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

05 Oct, 26

Salary

0.0

Posted On

07 Jul, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Agentic AI Systems, LLMs, RAG, CrewAI, LangGraph, AutoGen, Vector Search, Cloud-Native AI Services, API Development, Backend Engineering, Observability, Model Context Protocol, Multi-Agent Systems, Context Engineering, Software Development Life Cycle

Industry

Education

Description

Senior Software Engineer – AgentOps -------------------------------------------------------------------------------- About the Role We are looking for a Senior Software Engineer to design, build, and scale enterprise-grade agentic AI systems within the AgentOps. This role goes beyond traditional GenAI engineering - focusing on agent orchestration, context engineering, and production-grade AI systems that automate and transform business workflows through Agents. You will work at the intersection of LLMs, software engineering, and platform design, owning solutions end-to-end - from ideation to production - while ensuring reliability, safety, and measurable business impact. -------------------------------------------------------------------------------- Why Join AgentOps * Work on cutting-edge agentic AI systems at enterprise scale * Help build a symbiotic workforce of humans and AI agents * Solve real business problems across enterprise * Be part of shaping next-gen AI engineering practices -------------------------------------------------------------------------------- Key Responsibilities 1. Agentic System Design & Development * Design and build multi-agent systems and orchestrated workflows for enterprise use cases * Develop AI-powered solutions using existing SOTA LLMs from hyperscalers and enterprise data sources * Implement RAG pipelines, knowledge retrieval, and context-aware reasoning systems 2. AgentOps Lifecycle Ownership * Own full lifecycle: intake → design → validation → deployment → monitoring → improvement * Ensure Observability, evaluation frameworks, and guardrails are in place 3. Platform & Integration Engineering * Integrate AI solutions into Teams, web apps, and enterprise systems * Build APIs, backend services, and orchestration layers for scalable deployments * Should be well versed with one of the public cloud platform (AWS or Azure or GCP) 4. Observability, Reliability & Governance * Implement monitoring (latency, accuracy, failure modes) and logging pipelines * Ensure responsible AI practices, security, and compliance * Optimize models and workflows for cost, performance, and scalability 5. Innovation & Business Impact * Partner with product and business teams to identify high-impact agentic opportunities * Lead PoCs and rapidly scale successful solutions into production * Drive adoption of agentic workflows across business functions -------------------------------------------------------------------------------- Required Qualifications * Bachelor’s/master’s in computer science * 8–10+ years of software engineering experience, with strong AI/ML exposure * Strong technical foundation in Python and modern backend engineering patterns, with experience building APIs, services, and application components * Experience working with orchestration frameworks such as CrewAI, LangGraph, AutoGen, MAF or equivalent * Strong working knowledge of retrieval-augmented generation (RAG), embeddings, vector search, and grounding patterns * Experience building and deploying cloud-native AI services * Familiarity with observability and operational tooling such as Application Insights, OpenTelemetry, Azure Monitor, or New Relic, or equivalent monitoring platforms * Good collaboration and communication skills, with the ability to work effectively with engineers, architects, product owners, and platform teams * Strong ownership mindset across the SDLC, including design, build, testing, deployment, support, and continuous improvement * Experience building production Agentic systems (not just prototypes) * Exposure to Model Context Protocol (MCP), agent-to-agent (A2A) interaction patterns, or similar approaches to context exchange and distributed agent communication. About Pearson Pearson is the world's leading lifelong learning company, helping millions of learners worldwide achieve their educational and professional goals. Headquartered in London, Pearson provides digital learning solutions, educational content, assessments, certifications, English language learning, and workforce-skilling programs. With a strong focus on innovation, technology, and learner success, Pearson empowers individuals and organizations to thrive in an evolving global economy

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Responsibilities

Design and build enterprise-grade multi-agent AI systems and orchestrated workflows to automate business processes. Own the full lifecycle of AgentOps, including deployment, monitoring, and integration into enterprise platforms.