Senior Software Engineer – AgentOps
Pearson
About the role
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
- 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
- AgentOps Lifecycle Ownership
- Own full lifecycle: intake design validation deployment monitoring improvement
- Ensure Observability, evaluation frameworks, and guardrails are in place
- 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)
- 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
- 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
Who we are
At Pearson, our purpose is simple: to help people realize the life they imagine through learning. We believe that every learning opportunity is a chance for a personal breakthrough. We are the world's lifelong learning company. For us, learning isn't just what we do. It's who we are. To learn more: We are Pearson.
Pearson is an Equal Opportunity Employer and a member of E-Verify. Employment decisions are based on qualifications, merit and business need. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, gender expression, age, national origin, protected veteran status, disability status or any other group protected by law.
We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
If you are an individual with a disability and are unable or limited in your ability to use or access our career site as a result of your disability, you may request reasonable accommodations by emailing TalentExperienceGlobalTeam@grp.pearson.com.
Job: Engineering
Job Family: TECHNOLOGY
Organization: OCTO
Schedule: FULL_TIME
Workplace Type: Hybrid
Req ID: 24833
Eligibility & process
Who can apply
Employment type: Full-time
Skills & stack
Selection process
Details are shared by the recruiter after shortlisting.
Prep kit
19 resourcesStudy material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Explain the difference between Precision, Recall, and F1-Score. Answer
How does Gradient Descent work and what is the role of Learning Rate? Answer
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Behavioural & HR questions 3
Highlight Pearson's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.