Job Description

We are seeking a hands-on Senior AI Engineer with a strong foundation in traditional Machine Learning and practical, real-world experience building and deploying LLM- and GenAI-driven systems This role focuses on designing, engineering, and hardening production-grade AI solutions that are embedded into business workflows—not research prototypes.

You will work in small, high-impact delivery teams (2–3 engineers per initiative) and spend the majority of your time (~70–75%) building systems end to end, while also contributing to solution design, technical decision-making, and cross-functional collaboration.

Required Skills & Experience

Software & Systems Engineering

• 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).

AI / ML

• 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
  • LLM-based applications
• Ability to select ML vs. LLM-driven approaches based on business and operational constraints.

Cloud & DevOps

• 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.

Observability & Production Readiness

• Experience implementing logging, monitoring, and tracing for production systems.
• Familiarity with system resilience patterns such as:
  • Rate limiting
  • Failover strategies
  • Kill-switch mechanisms

Problem Solving & Mindset

• 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.

Communication & Collaboration

• Experience working in cross-functional teams.
• Ability to clearly articulate technical and business trade-offs, including:
  • LLM vs traditional ML
  • Build vs buy decisions
  • Speed vs robustness

Good to Have

• Experience with enterprise AI platforms or internal AI frameworks.
• Prior production experience with:
  • Agentic architectures
  • Multi-agent systems
  • RAG-based systems at scale
• Exposure to AI governance, safety, and compliance considerations.
• Experience mentoring junior engineers or owning technical modules.
• Hands-on experience optimizing performance and cost for AI workloads.

Key Responsibilities AI Solution Design & Problem Solving

• 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.

Hands-on Engineering & Delivery (70–75%)

• 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.

ML & Generative AI Expertise

• 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).
• Collaboration & Technical Leadership (25–30%)
• 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.

Bachelor's degree in relevant field with 8-10 years of relevant experience

Why EXL

At EXL, you’ll work at the intersection of life sciences, cloud modernization, and AI — helping clients build data ecosystems that drive better outcomes, accelerate research, and power next-generation analytics.

Expected Hours of Work:

Employees are required to work 40 hours per week. This role may require more than 40 hours per week and/or weekends.

Salary: $120,000.00-$140,000.00 per year, commensurate with experience. This range is provided as a general guideline and may vary based on qualifications, skills, and location.

Base Salary Range Disclaimer: The base salary range represents the low and high end of the EXL base salary range for this position. Actual salaries will vary depending on factors including but not limited to: location and experience The base salary range listed is just one component of EXL's total compensation package for employees Other rewards may include bonuses, as well as a Paid Time Off policy, and many region specific benefits.

To view our total rewards offered click here —> https://www.exlservice.com/us-careers-and-benefits

EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for 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 54,000 employees spanning six continents. For more information, visit www.exlservice.com EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL’s Human Resources team, as well as our hiring managers.