Interested in this AI/ML Engineer role at Cognizant?
Apply Now →Skills & Technologies
About This Role
Job Summary We are seeking a highly skilled and innovative Senior AI/ML Engineer with strong expertise in Python, PySpark, Azure Machine Learning, Generative AI, and Full Stack Development to design and deliver advanced analytics and AI\-driven solutions for global investment banking and brokerage operations. The ideal candidate will combine deep technical expertise in machine learning, distributed computing, cloud\-native AI platforms, and modern AI frameworks such as LangChain, LangGraph, RAG, Agentic AI Frameworks, FastAPI, and Azure OpenAI Service . This role requires close collaboration with business stakeholders to transform complex financial data into actionable insights that improve decision\-making, reduce operational risk, and enhance operational efficiency. Key Responsibilities Machine Learning \& Advanced Analytics
Design, develop, and deploy advanced machine learning models using Python and PySpark to analyze large\-scale financial datasets and generate actionable business insights.
Build predictive, classification, clustering, anomaly detection, forecasting, and risk models supporting investment banking and brokerage functions.
Perform rigorous model validation, back\-testing, and experimentation using historical and simulated market data.
Evaluate and implement appropriate statistical, machine learning, deep learning, and AI techniques based on business requirements and regulatory considerations.
Optimize model performance through feature engineering, hyperparameter tuning, algorithm enhancements, and distributed computing techniques. Generative AI \& Agentic Solutions
Design and implement enterprise\-grade Generative AI solutions using Azure OpenAI Service .
Build and deploy Retrieval\-Augmented Generation (RAG) applications leveraging vector databases and knowledge retrieval systems.
Develop intelligent agent\-based systems using LangChain, LangGraph, and Agentic AI frameworks to automate business workflows and enhance decision support.
Apply Natural Language Processing (NLP), Large Language Models (LLMs), document intelligence, and conversational AI to streamline surveillance, reporting, compliance, and advisory functions.
Ensure safe, responsible, and governed adoption of Generative AI capabilities across the organization. Python Full Stack Development
Design and develop scalable backend services and APIs using FastAPI .
Build microservices and AI application frameworks that integrate machine learning and GenAI capabilities into enterprise ecosystems.
Develop reusable and maintainable software components following modern software engineering best practices.
Implement API integrations, authentication mechanisms, monitoring, logging, and performance optimization strategies. Data Engineering \& MLOps
Design and implement scalable data pipelines and feature engineering workflows using Azure Machine Learning and cloud\-native services.
Build reusable data products and machine learning components supporting multiple analytics and AI initiatives.
Partner with Data Engineering teams to operationalize machine learning models and AI applications.
Establish model monitoring, retraining strategies, experiment tracking, and lifecycle management processes.
Ensure solutions are secure, reliable, scalable, and production\-ready. Cloud \& Azure AI Platform
Develop end\-to\-end ML and AI solutions using:
Azure Machine Learning
Azure OpenAI Service
Azure Data Lake
Azure Databricks
Azure Storage Services
Azure DevOps
Manage model deployment, monitoring, governance, and operationalization on Azure platforms.
Support enterprise\-scale AI and analytics workloads while maintaining compliance and security standards. Business Collaboration
Collaborate with product owners, business analysts, operations teams, and technology stakeholders to define high\-value data science initiatives.
Translate complex investment banking and brokerage business challenges into measurable analytical solutions.
Present recommendations and analytical findings to both technical and non\-technical audiences.
Drive adoption of AI and machine learning solutions through effective communication and stakeholder engagement. Governance \& Responsible AI
Promote responsible AI practices by evaluating model fairness, explainability, bias, security, and data quality.
Document assumptions, risks, methodologies, and limitations in a transparent and accessible manner.
Ensure adherence to regulatory requirements, model governance frameworks, and enterprise AI policies. Leadership \& Mentoring
Mentor junior data scientists, machine learning engineers, and developers.
Promote best practices in software development, experimentation, MLOps, AI engineering, and model governance.
Contribute to a culture of innovation, continuous learning, and technical excellence. Required Qualifications Technical Skills
8\+ years of experience in Data Science, Machine Learning, AI Engineering, or related fields.
Expert\-level proficiency in Python and PySpark for large\-scale data processing and model development.
Strong experience with:
FastAPI
REST APIs
Microservices Architecture
Object\-Oriented Programming
Software Engineering Best Practices
Hands\-on experience with:
LangChain
LangGraph
RAG Architectures
Agentic AI Frameworks
LLM Application Development
Strong expertise in:
Azure Machine Learning
Azure OpenAI Service
Azure Databricks
Azure Data Lake
MLOps and CI/CD Practices
Experience developing and deploying enterprise\-grade AI/ML solutions in cloud environments. Machine Learning \& AI
Deep understanding of:
Supervised Learning
Unsupervised Learning
Deep Learning
Ensemble Methods
NLP
Time\-Series Forecasting
Anomaly Detection
Risk Modeling
Strong understanding of model evaluation, feature engineering, experimentation, validation, and explainability. Domain Experience
Prior experience supporting:
Investment Banking
Capital Markets
Brokerage Operations
Trade Surveillance
Risk Management
Front Office or Middle Office Functions
Understanding of financial products, market data, and regulatory expectations is highly desirable. Soft Skills
Excellent communication and stakeholder management skills.
Ability to explain complex technical topics to non\-technical audiences.
Strong analytical and problem\-solving capabilities.
Experience working effectively within distributed and hybrid teams. Preferred Qualifications
Experience with vector databases such as Pinecone, Azure AI Search, Weaviate, or ChromaDB.
Knowledge of containerization technologies including Docker and Kubernetes.
Experience with CI/CD pipelines and DevOps practices.
Exposure to Responsible AI, Model Risk Management, and AI governance frameworks.
Azure certifications in AI, Data Science, or Machine Learning.
- Please note this role is not able to offer visa transfer or sponsorship now or in the future\* We're excited to meet people who share our mission and who can make an impact in a variety of ways. Don't hesitate to apply—even if you only meet the minimum requirements. Think about your transferable experiences and unique skills that make you stand out. Salary and Other Compensation: Applications will be accepted until Sept 07, 2026, The annual salary for this position is between $ 90,000 \- $ 150,000 depending on experience and other qualifications of the successful candidate. This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans. Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
Medical/Dental/Vision/Life Insurance
Paid holidays plus Paid Time Off
401(k) plan and contributions
Long\-term/Short\-term Disability
Paid Parental Leave
Employee Stock Purchase Plan Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Salary Context
This $90K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cognizant, 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. This role's midpoint ($120K) sits 44% below the category median. Disclosed range: $90K to $150K.
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.
Cognizant AI Hiring
Cognizant has 24 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Architect, AI Agent Developer. Positions span Juno Beach, FL, US, Pleasanton, CA, US, Rockville, MD, US. Compensation range: $99K - $405K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.