Note: The job is a remote job and is open to candidates in USA. EXL is seeking a hands-on Senior AI Engineer with a strong foundation in traditional Machine Learning and practical experience in building and deploying LLM- and GenAI-driven systems. This role focuses on designing, engineering, and hardening production-grade AI solutions that are integrated into business workflows.
Responsibilities
- Partner with business and product stakeholders to translate real-world problems into practical AI solutions
- Determine when to apply: Traditional ML approaches (classification, regression, clustering, recommendation systems) LLM / GenAI approaches, including agentic workflows
- Evaluate and communicate trade-offs across accuracy, cost, latency, scalability, and operational complexity
- Design iterative AI workflows and propose alternative solution approaches where applicable
- Build and own end-to-end AI systems, including: Data ingestion and processing pipelines Feature engineering and prompt construction ML and LLM integration and orchestration API-based AI services for downstream consumption
- Deploy and harden production AI systems with: Error handling and fallback mechanisms Guardrails, safety controls, and exception handling Observability (logging, metrics, tracing, dashboards)
- Ensure production readiness through: Performance tuning and latency optimization Cost management and optimization strategies Scalability and reliability planning
- Implement AI system controls such as: Input validation and prompt injection mitigation Configurable policies and kill switches
- Transition PoCs into production-grade systems through refactoring, testing, and system hardening
- Apply strong fundamentals in traditional ML, including supervised and unsupervised learning techniques
- Build and deploy GenAI solutions, with experience across at least one or two real-world LLM implementations
- Work with modern LLMs (e.g., OpenAI, Claude, Gemini, Llama or equivalent models)
- Design and implement RAG (Retrieval-Augmented Generation) architectures
- Apply prompt engineering, evaluation techniques, and iterative optimization
- Build and evolve tool-based and agentic workflows, including multi-agent systems
- Use agent orchestration frameworks (e.g., LangChain, LangGraph, or equivalent custom systems)
- Act as a senior technical contributor within small delivery teams
- Debug complex AI system behavior and production issues beyond prompt-level tuning
- Contribute to architectural and design decisions alongside architects and platform teams
- Collaborate closely with: Product managers and business stakeholders Platform, cloud, and infrastructure teams
- Uphold strong software engineering practices and delivery discipline
Skills
- 10-12 years of overall software engineering experience, including prior work as an ML Engineer or equivalent
- Strong backend development skills (Python, Java, Node.js, or similar languages)
- Experience designing and building REST or gRPC-based services
- Solid understanding of distributed system design
- Containerization and orchestration experience (Docker, Kubernetes)
- Hands-on experience across traditional ML and modern GenAI systems
- Proficiency with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalents
- Experience building or deploying ML-driven production systems
- Experience building or deploying LLM-based applications
- Ability to select ML vs. LLM-driven approaches based on business and operational constraints
- Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP)
- Experience with CI/CD pipelines and deployment automation
- Understanding of model, code, and configuration versioning best practices
- Experience implementing logging, monitoring, and tracing for production systems
- Familiarity with system resilience patterns such as rate limiting, failover strategies, and kill-switch mechanisms
- Strong ability to solve ambiguous, real-world engineering problems
- Comfortable working in fast-moving, iterative environments
- Ownership mindset with a bias toward practical, scalable solutions
- Experience working in cross-functional teams
- Ability to clearly articulate technical and business trade-offs, including LLM vs traditional ML, build vs buy decisions, and speed vs robustness
Benefits
- Hybrid (3 days/week in office)
- Annual Bonus
- For more information on benefits and what we offer please visit us at [US Careers and Benefits](https://www.exlservice.com/us-careers-and-benefits)
Company Overview
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