Job Description: EXL (NASDAQ: EXLS ) is a global data and artificial intelligence ("AI") company that offers services and solutions to reinvent client business models, drive better outcomes and unlock growth with speed. EXL harnesses the power of data, AI, and deep industry knowledge to transform businesses, including the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect.
We are headquartered in New York and have more than 60,000 employees spanning six continents. For more information, visit www.exlservice.com.
Role Title: Senior Engineer, Applied AI
BU/Segment: Digital, AI Innovation and R&D
Location: Dublin, Republic of Ireland (Flexible hybrid working)
Employment Type : Permanent
Responsibilities: Summary of the role:
We are seeking a hands-on Senior Engineer in EXL’s AI Innovation and R&D team to build and productionize Generative AI and Agentic AI capabilities for client-facing initiatives and internal accelerators. This is an AI-first engineering role focused on agentic workflows, advanced retrieval systems, evaluation, and production readiness, with strong software engineering fundamentals to ship reliable features. You will collaborate closely with data science, backend engineering, and delivery teams to implement scalable, maintainable AI systems and contribute to EXL’s rapid innovation cadence.
As part of your duties, you will be responsible for:
AI Agent Engineering
- Build and own production-grade agent systems end to end, from design through deployment and ongoing operation, including steady-state maintenance, reliability improvements, and operational support.
- Implement stateful, durable agentic workflows with clear checkpoints, safe retries, and human-in-the-loop steps for high-impact actions.
- Design agent architectures with planning, tool use, memory, and escalation, and proactively address common failure modes such as hallucinations, tool misuse, state errors, and loops.
- Build secure tool integrations using MCP-based connectors and/or tool registries to expose internal services and approved external SaaS APIs to agents.
- Implement advanced retrieval and grounding, including hybrid retrieval (vector plus structured), reranking, relevance tuning, and robust context assembly for grounded responses.
- Treat evaluation as an engineering discipline by creating offline datasets, regression gates, and online monitoring, and defining measurable success metrics such as task success, groundedness, and tool-call correctness that can gate releases.
- Instrument AgentOps and LLMOps by tracing full trajectories (retrieval, model calls, tool calls, outputs), attributing cost and latency per run, and alerting on drift and failure patterns using standard observability practices and tools.
- Develop self-improving and self-evolving agent loops using evaluator-driven optimization (generate, evaluate, refine) and/or RL-style approaches where rewards are verifiable (tests, constraints, correctness checks).
- Use agentic coding workflows beyond autocomplete, such as Cursor, Replit, Claude Code, Codex to accelerate delivery while maintaining engineering standards, and standardize repo guidance via AGENTS.md and CLAUDE.md-style instruction files.
- Stay current with fast-moving GenAI and Agentic AI tooling and research, and translate relevant innovations into pragmatic implementations
Backend Engineering
- Optimize systems for cost, latency, and quality using pragmatic routing (planner versus executor), caching, and model/provider selection across hosted and local models.
- Build robust backend services that power agents, including Python services and production APIs (for example FastAPI), Postgres, and event-driven or job execution patterns (for example Kafka, Airflow, workflow engines), with strong integration hygiene (OAuth, webhooks, rate limits, idempotency).
- Deploy and operate in real environments using Docker and Kubernetes, CI/CD, and infrastructure best practices, and contribute to runbooks and operational readiness as part of shipping.
Delivery and collaboration
- Partner with cross-functional teams to translate problem statements into implementable AI solutions, and deliver components that fit broader architectures.
- Contribute to engineering best practices, documentation, and reusable components that accelerate delivery across projects.
- Participate in a culture of applied innovation where strong ideas are not only shipped, but also captured as patents, whitepapers, and technical write-ups for broader reuse and recognition.
Qualifications: Qualifications and experience we consider to be essential for the role:
- Experience: Typically 4 to 7 years in software engineering, applied ML, or applied AI, with demonstrated hands-on delivery ownership.
- Programming: Strong proficiency in Python; working experience with modern software engineering practices (testing, code reviews, version control).
- GenAI and LLM application development: Experience building LLM-powered applications, including prompt design, evaluation, and production considerations.
- Agentic frameworks: Practical experience with frameworks such as LangChain, LlamaIndex, Langfuse, LangGraph, CrewAI, or similar; understanding of agent planning, tool use, multi-agent collaboration.
- Retrieval systems: Hands-on experience with embeddings and vector databases (Milvus, Pinecone, Weaviate, Chroma, FAISS or similar), plus retrieval tuning and grounding patterns.
- AI architecture patterns: Understanding of microservices and event-driven architectures, ReAct, Self-Evolve and multi-agent systems.
- Cloud: Practical experience deploying services on at least one major cloud platform (Azure, AWS, GCP), including containerization and CI/CD.
- Production mindset: Familiarity with LLMOps practices such as automated testing, monitoring, governance, and operational readiness for LLM applications.
Skills and Personal attributes we would like to have:
- Experience with knowledge graphs, structured retrieval, complex task decomposition, and evaluation methodologies for agentic systems.
- Experience with synthetic data generation, auto-labeling approaches, or data-centric iteration for evaluation sets.
- Exposure to developer copilots or rapid prototyping tools (Cursor, Windsurf, Replit, GitHub Copilot, Claude Code, Codex).
- Prior experience supporting client-facing delivery teams or building reusable accelerators used across multiple engagements.
What success looks like
- Shipped agentic and retrieval components that integrate cleanly into broader solutions, with measurable improvements in quality, reliability, and delivery speed.
- Repeatable evaluation and monitoring practices that reduce regressions and enable safe iteration.
- Strong collaboration with AVP and backend teams, owning AI feature delivery while leveraging platform specialists for deeper infrastructure work.
As part of a leading global Data and AI company, you can look forward to:
- A competitive salary with a generous bonus, private healthcare, life assurance at 4 x your annual salary, income protection insurance, and a rewarding pension.
- At EXL, we are committed to providing our employees with the tools and resources they need to succeed and excel in their careers. We offer a wide range of professional and personal development opportunities. We also support a range of learning initiatives that allow our employees to build on their existing skills and knowledge. From online courses to seminars and workshops, our employees have the opportunity to enhance their skills and stay up to date with the latest trends and technologies.
- As an Equal Opportunity Employer, EXL is committed to diversity. Our company does not discriminate based on race, religion, colour, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, age, or disability status.
- At EXL, we offer a flexible hybrid working model that allows employees to live a balanced, healthy lifestyle while strengthening our culture of collaboration.
To be considered for this role, you must already be eligible to work in the Republic of Ireland.
