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About This Role
Overview:
Prodapt is the largest and fastest\-growing specialized player in the Connectedness industry, recognized by Gartner as a Large, Telecom\-Native, Regional IT Service Provider across North America, Europe and Latin America. With its singular focus on the domain, Prodapt has built deep expertise in the most transformative technologies that connect our world. Prodapt is a trusted partner for enterprises across all layers of the Connectedness vertical. Prodapt designs, configures, and operates solutions across their digital landscape, network infrastructure, and business operations – and craft experiences that delight their customers. Today, Prodapt’s clients connect 1\.1 billion people and 5\.4 billion devices, and are among the largest telecom, media, and internet firms in the world. Prodapt works with Google, Amazon, Verizon, Vodafone, Liberty Global, Liberty Latin America, Claro, Lumen, Windstream, Rogers, Telus, KPN, Virgin Media, British Telecom, Deutsche Telekom, Adtran, Samsung, and many more. A “Great Place To Work® Certified™” company, Prodapt employs over 6,000 technology and domain experts in 30\+ countries across North America, Latin America, Europe, Africa, and Asia. Prodapt is part of the 130\-year\-old business conglomerate The Jhaver Group, which employs over 30,000 people across 80\+ locations globally.
We are seeking an experienced Senior Generative AI / Machine Learning Engineer with 10\+ years of experience to design, develop, and deploy enterprise AI solutions for one of our clients remotely. The ideal candidate will have strong expertise in Generative AI, Machine Learning, Large Language Models (LLMs), cloud platforms, and MLOps, with experience delivering scalable, production\-ready AI solutions in consulting or managed services environments.
Responsibilities:
Key Responsibilities
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- Design, develop, and deploy enterprise\-grade Generative AI and Machine Learning solutions for client engagements.
- Build AI\-powered applications using Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), AI Agents, and prompt engineering techniques.
- Collaborate with client stakeholders to understand business requirements and translate them into scalable AI solutions.
- Develop, fine\-tune, evaluate, and optimize foundation models for enterprise use cases.
- Design scalable data pipelines and end\-to\-end ML workflows for model training, deployment, monitoring, and lifecycle management.
- Implement MLOps best practices, including CI/CD, model versioning, monitoring, governance, and automated retraining.
- Integrate AI solutions with enterprise applications, APIs, and cloud\-native services.
- Ensure AI solutions meet security, compliance, performance, and reliability standards.
- Provide technical leadership, mentor engineering teams, and contribute to solution architecture and pre\-sales activities.
- Support client implementations, troubleshooting, and continuous optimization of deployed AI solutions.
- Stay current with emerging AI technologies, frameworks, and industry best practices.
Requirements:
Required Skills
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- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, Mathematics, Statistics, or a related technical field. Master's degree is preferred.
- 10\+ years of experience in Machine Learning, Artificial Intelligence, Data Science, or software engineering.
- 1\+ years of hands\-on experience in Generative AI and Large Language Model (LLM) application development.
- Strong programming expertise in Python and experience with REST APIs.
- Hands\-on experience with TensorFlow, PyTorch, Scikit\-learn, Hugging Face Transformers, and related ML frameworks.
- Experience working with LLMs such as OpenAI GPT, Llama, Claude, Gemini, or similar foundation models.
- Strong expertise in RAG, prompt engineering, AI Agents, function calling, and LLM evaluation.
- Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar AI orchestration frameworks.
- Experience with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or FAISS.
- Strong knowledge of MLOps using MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar platforms.
- Experience with AWS, Microsoft Azure, or Google Cloud Platform.
- Experience with Docker, Kubernetes, Git, CI/CD pipelines, and containerized deployments.
- Strong understanding of SQL, NoSQL databases, distributed data processing, and microservices architecture.
- Experience integrating AI solutions with enterprise platforms and business applications.
- Strong communication, client engagement, presentation, and stakeholder management skills.
- Experience working in Agile/Scrum delivery environments.
Preferred Skills
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- Experience in consulting, managed services, or client\-facing enterprise delivery.
- Knowledge of Responsible AI, AI governance, model security, and compliance frameworks.
- Experience with Azure OpenAI Service, Amazon Bedrock, Google Vertex AI, or similar enterprise AI platforms.
- Familiarity with multi\-agent AI systems, multimodal AI, and AI workflow automation.
- Relevant cloud or AI certifications are a plus.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Prodapt Solutions, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Prodapt Solutions AI Hiring
Prodapt Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Richardson, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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