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
cutting-edge agentic AI systems at enterprise scale
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
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
intake → design → validation → deployment → monitoring → improvement
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 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
