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About This Role
Description \-
We are seeking an experienced Senior AI Engineer to design and deliver scalable AI\-driven solutions that enable advanced analytics, intelligent decision\-making, and business process optimization. The ideal candidate will possess strong expertise in AI solution architecture, Python development, advanced SQL, Azure cloud technologies, and AI\-powered analytical platforms. Experience in financial services and regulated environments is highly desirable.
Responsibilities
AI Solution Architecture
- Design end\-to\-end AI and analytics solutions that support predictive, generative, and intelligent automation use cases.
- Develop scalable architectures that support model training, inference, feature generation, and analytical workloads.
- Ensure solutions are resilient, extensible, and aligned with enterprise architecture standards.
- Establish traceability and explainability across AI workflows and outputs.
- Drive architecture decisions that support future AI initiatives and scalability requirements.
Python Development for AI
- Develop clean, modular, and maintainable Python applications for AI and analytics solutions.
- Build reusable components and frameworks supporting machine learning and generative AI workloads.
- Implement robust error handling, logging, monitoring, and automated testing practices.
- Integrate AI/ML models into enterprise applications and business workflows.
- Optimize code performance and maintain high engineering standards.
Advanced SQL Analytical Engineering
- Develop and optimize complex SQL queries using CTEs, window functions, aggregates, and advanced analytical patterns.
- Support large\-scale analytical workloads and performance tuning initiatives.
- Design efficient approaches for entity resolution, relationship discovery, and AI\-driven analytics.
- Ensure accuracy and consistency in analytical outputs and reporting.
Azure AI Cloud Platform Engineering
- Design and implement secure, scalable AI solutions on Microsoft Azure.
- Utilize Azure\-native services to support AI model deployment, monitoring, and governance.
- Implement robust security controls including RBAC, managed identities, and compliance requirements.
- Ensure proper environment strategy across Development, Test, and Production environments.
- Optimize cloud resource utilization and cost efficiency.
AI Mapping Relationship Analytics
- Design solutions supporting entity mapping, relationship modeling, network analysis, and knowledge graph use cases.
- Develop reusable AI features that accelerate future AI initiatives.
- Enable pattern discovery through intelligent contextual modeling and analytical frameworks.
- Support integration of internal and external sources to enrich AI\-driven insights.
- Build architectures that can be reused across multiple AI and business scenarios.
Financial Services Governance
- Design AI solutions that meet regulatory, compliance, and audit requirements.
- Ensure explainability, traceability, and governance of AI\-generated outcomes.
- Work with business stakeholders across risk, operations, compliance, and analytics functions.
- Apply strong quality controls to ensure accuracy and reliability of AI\-driven insights.
Required Skills
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, Data Science, or related field.
- 8\+ years of software engineering experience with at least 4\+ years focused on AI/ML solutions.
- Expert\-level Python programming skills.
- Strong SQL expertise including query optimization and analytical processing.
- Hands\-on experience with Microsoft Azure cloud platform and AI services.
- Experience designing and deploying enterprise\-scale AI solutions.
- Strong understanding of AI/ML lifecycle, model deployment, monitoring, and governance.
- Experience implementing secure and compliant cloud architectures.
Preferred Skills
- Experience with Generative AI, LLMs, RAG, Knowledge Graphs, and Agentic AI frameworks.
- Experience with Azure OpenAI, Azure AI Foundry, Azure Machine Learning, and Cognitive Services.
- Familiarity with graph analytics, relationship modeling, and entity resolution.
- Financial Services domain experience including Risk, Compliance, Asset Management, Banking, or Capital Markets.
- Exposure to MLOps, CI/CD pipelines, and AI governance frameworks.
Key Skills
- AI Solution Architecture
- Generative AI LLMs
- Azure OpenAI Azure AI Services
- Python Development
- Advanced SQL
- Azure Cloud Architecture
- AI Governance Explainability
- Entity Resolution Relationship Modeling
- Knowledge Graphs
- Machine Learning Integration
- Model Deployment Monitoring
- Financial Services Domain
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Coforge, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Coforge AI Hiring
Coforge has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oaks, PA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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