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