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About This Role
Job Description: Innovative Senior Software ML Engineer with over 8 years of experience in developing scalable AI Agentic frameworks, ML pipelines, and cloud\-native applications. Expertise in Python, Langchain/LangGraph, and RAG systems. Proven ability to design end\-to\-end solutions, mentor teams, and drive cross\-functional collaboration to achieve impactful results
Job Title: Vice President L1, Senior AI Forward Deployed Engineer
EXL (NASDAQ: EXLS) is seeking a visionary and technically adept Vice President (VP) in EXL’s AI Innovation \& R\&D team to spearhead our innovative client\-facing initiatives in the rapidly evolving fields of Generative and Agentic AI. This high\-impact, senior consulting role requires a unique blend of deep technical expertise in AI solutioning and deployment, Fortune 500 client stakeholder management skills, and the ability to drive thought leadership. You will be instrumental in advising Insurance, Healthcare, and Banking clients, designing cutting\-edge AI solutions, leading complex implementations, and shaping EXL’s strategy and market presence in Generative and Agentic AI. You will collaborate closely with data science, technology, business development, and client teams to identify opportunities, architect robust solutions, and deliver transformative results.
- Responsibilities: Software Engineering: Innovative Senior Software Engineer experience in developing scalable AI Agentic frameworks, ML modeling pipelines, and cloud\-native applications. Expertise in Python, Langchain/LangGraph, and RAG systems. Proven ability to design end\-to\-end solutions, mentor teams, and drive cross\-functional collaboration to achieve impactful results
- AI Architecture Innovation Research \& Design: Our mantra is Innovation at Speed. Focus on new innovative methods of designing and architecting AI and GenAI systems and be able to grasp and adapt monthly and weekly AI innovation coming out in the industry and apply it to our clients’ use cases and needs in new modern ways.
- Client Advisory \& Solutioning: Engage directly with senior client stakeholders (including C\-suite) to understand complex business challenges, identify opportunities for GenAI and Agentic AI, and define project scope.
- Domain background: Any domain background in Insurance, Trading, Banking, Credit Risk, Healthcare, and Finance.
- Workshop Facilitation: Design, lead, and facilitate high\-impact client workshops and strategy sessions focused on identifying and prioritizing Generative and Agentic AI use cases and roadmap development.
- Technical Leadership \& Architecture: Design, architect, and oversee the development and deployment of scalable, robust, and cutting\-edge Generative AI and sophisticated Agentic AI systems (including multi\-agent workflows) for client and internal projects.
- Project \& Engagement Leadership: Lead large\-scale, complex Generative AI and Agentic AI projects from strategic conception through successful deployment, managing cross\-functional teams (internal and client\-side) and ensuring timely delivery of high\-quality solutions.
- Technical Mentorship: Mentor and guide technical teams (data scientists, data and AI engineers) in best practices for advanced AI development, deployment, MLOps/LLMOps, and agentic system design.
- Stakeholder Management: Build and maintain strong relationships with key internal and external stakeholders, effectively communicating complex technical concepts and project progress.
- Quality \& Best Practices: Ensure adherence to rigorous software engineering principles, Agile methodologies, and responsible AI practices throughout the solution lifecycle.
- Stay Current: Maintain deep expertise in the latest trends, research, tools, and technologies within Generative AI, Large Language Models (LLMs), and Agentic AI paradigms.
Qualifications: Technical Skills:
- Combined skills: Python, Langchain, LangGraph, RAG systems, AI Agents, Agentic Frameworks, ScikitLearn, Numpy, Pandas, Gradient Boost models, Ensembles, Reinforcement Learning, LSTMs, Transformers, RlLib, AWS SageMaker, AWS BedRock, NLP, ML pipelines, Docker, Kubernetes, Terraform, FastAPI, PostgreSQL, ReactJS, Redis, GCP, CI/CD (Jenkins, GitHub Actions), Prompt engineering, cross\-functional collaboration, mentorship, iterative development, analytics automation, scalable AI framework
- Programming \& Libraries : Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Experience using Cursor, Windsurf, Replit, and Github Copilot.
- LLM Expertise : Proven experience developing applications leveraging state\-of\-the\-art LLMs (e.g., GPT series, Llama series, Mistral, Claude) including prompt engineering, fine\-tuning, and evaluation.
- GenAI \& Agentic Frameworks : Hands\-on mastery of core GenAI frameworks (e.g., LangChain, LlamaIndex, Langfuse) and practical experience with Agentic AI frameworks and concepts (e.g., AutoGen, CrewAI, LangGraph, agent planning, tool use integration, multi\-agent collaboration).
- AI Architecture : Deep understanding of AI/ML system architecture patterns, including microservices, event\-driven architectures, and patterns specific to RAG (Retrieval\-Augmented Generation), Graph RAG, Agentic RAG, and multi\-agent systems.
- Data: Knowledge of industry approaches to data engines and data labeling like Scale.ai and Mercor. Experience with auto data\-labeling and synthetic data generation techniques.
- Vector Databases \& Embeddings : Expertise in working with various embedding models and vector databases (e.g., Pinecone, Weaviate, Chroma, FAISS).
- Advanced AI Concepts : Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning engines, knowledge graphs, autonomous agent design, and evaluation methodologies for complex AI systems.
- Software Engineering : Strong foundation in software engineering principles for building scalable, maintainable, and production\-ready AI systems.
- Cloud Platforms : Strong working knowledge and practical deployment experience on at least one major cloud platform (AWS, Azure, GCP), including their AI/ML services.
- LLMOps/MLOps : Expertise in designing and implementing robust MLOps/LLMOps pipelines for automated testing, CI/CD, monitoring, and governance of complex AI models and applications.
Leadership \& Communication:
- Proven ability to lead and motivate diverse, global teams
- Excellent communication skills, capable of explaining complex AI concepts to various stakeholders
Strong project and program management skills and experience working in Agile environments
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Qualifications:
- Education: Bachelor's degree in computer science, AI, Machine Learning, or a related quantitative field. Master's or Ph.D. strongly preferred.
- Experience: Minimum 10 years of experience in AI/ML/Data Science, with at least 5 years in significant leadership roles involving solution architecture, team management, and project delivery.
- Deployment Success: Demonstrated track record of successfully architecting and deploying large\-scale AI projects, preferably including complex GenAI and/or Agentic AI applications in enterprise or client settings.
- Consulting Background: Prior experience in technology consulting or a client\-facing technical specialist role within a technology provider is highly advantageous.
- Global Experience: Experience working effectively with global teams across multiple geographic locations is a plus.
Additional Considerations:
We seek a leader who excels at translating cutting\-edge Generative and Agentic AI capabilities into tangible business value for clients and the organization. The ideal candidate bridges the gap between advanced AI research and practical, robust, scalable, and ethical business applications, possessing both deep hands\-on technical credibility and strategic consulting acumen to architect and implement AI solutions at an enterprise scale.
We Offer:
- The opportunity to work on pioneering Generative and Agentic AI projects with a talented, global team.
- A platform to influence AI strategy and adoption for major clients across various industries.
- A competitive salary and comprehensive benefits package.
- A supportive, collaborative, and innovative work environment.
The chance to be a visible leader and make a significant impact on the future of AI application.
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The typical base pay range for this role across the U.S. is USD $200,000 \- $280,000 per year.
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us\-careers\-and\-benefits
The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale.
Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position.
The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher\-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
Salary Context
This $200K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At EXL Service, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. This role's midpoint ($240K) sits 32% above the category median. Disclosed range: $200K to $280K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
EXL Service AI Hiring
EXL Service has 9 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Architect. Positions span US, Jersey City, NJ, US. Compensation range: $150K - $280K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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