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About This Role
The Agentic AI Data Engineer is a hands\-on role focused on building and maintaining the data pipelines and infrastructure that fuel AI agent systems. Within TCS’s AI \& Data group (Americas), you will be the builder who turns data architecture plans into reality, ensuring that AI models and agents have continuous access to high\-quality, timely data. This client\-facing consulting role involves hybrid work from client site as needed for deployment. You’ll work across wide array of business functions within Retail. By combining expertise in data ingestion, transformation, and integration with knowledge of AI data needs, you will play a critical part in enabling AI agents to perform reliably and accurately in production.
What You Would Be Doing:
Build Data Ingestion Pipelines: Develop robust pipelines to extract data from various sources (databases, APIs, flat files, streaming sources) relevant to the AI solution.
Data Transformation \& Processing: Implement transformation and cleaning steps on raw data to make it usable for AI, ensuring efficiency and scalability.
Loading Data to Storage/Indices: Set up processes to load processed data into target storage systems that AI agents or models will use.
Real\-Time Data Feeds: Implement streaming or incremental update pipelines when AI systems require real\-time or frequently updated data.
Pipeline Automation \& Scheduling: Use orchestrators or schedulers to automate the data workflows.
Data Integration \& API Development: Develop and maintain integration components for real\-time data fetching.
Collaborate on RAG/Knowledge Base Updates: Work closely with AI Data Architects on implementing RAG updates.
Testing and Validation of Data Pipelines: Develop tests and monitoring for your data pipelines.
Optimize Pipeline Performance: Profile and optimize data pipelines for speed and resource usage.
Documentation and Handover: Document pipeline processes, configurations, and dependencies clearly.
Industry\-Specific Data Handling: Adapt data engineering to specific domain needs.
Collaboration \& Agile Implementation: Work as part of an agile product team, collaborating with data architects, AI engineers, and others.
Maintain and Evolve Pipelines: Monitor pipelines and handle maintenance post go\-live.
What Skills Are Expected:
Programming \& Scripting: Strong programming skills, especially in Python, and experience with other languages like SQL.
Data Pipeline Development: Practical experience building data pipelines end\-to\-end.
Database and SQL Skills: Proficiency in writing and optimizing SQL queries.
Big Data \& Distributed Processing: Experience with big data technologies like Apache Spark.
Streaming Data Experience: Familiarity with streaming frameworks and tools like Kafka.
API Integration and Web Services: Ability to interact with web APIs for data ingestion or extraction.
Data Formats and Parsing: Strong understanding of data formats and ability to parse JSON, XML, or custom text formats.
DevOps for Data Pipelines: Basic DevOps skills, including using Git for version control and CI/CD pipelines.
Problem Solving \& Debugging: Strong ability to troubleshoot data issues.
Data Quality Focus: Attentiveness to data quality and skills in implementing checks and validating outputs.
Collaboration \& Commun ication: Good communication skills to work with the team and clients.
Time Management \& Flexibility: Ability to handle multiple tasks and prioritize effectively.
Domain Data Understanding: Aptitude to learn domain context from data.
Security \& Privacy Business Units: Understanding of handling sensitive data securely in pipelines.
Continuous Learning: Willingness to learn new tools or frameworks as needed.
Key Technology Capabilities:
ETL / Data Integration Tools: Experience with tools such as Apache Airflow, Informatica PowerCenter, or cloud\-based ones like Azure Data Factory.
Big Data Processing: Proficiency in Apache Spark and knowledge of Hadoop HDFS.
SQL \& Databases: Strong practical SQL skills and familiarity with relational database systems.
NoSQL and Other Data Stores: Knowledge of specific systems like MongoDB or Cassandra.
Stream Processing: Hands\-on usage of Apache Kafka and understanding of consumer group mechanics.
Cloud Storage \& Compute: Familiarity with cloud storage services like Amazon S3 and cloud compute for ETL.
APIs \& Web Services: Experience building or using connectors to RESTful APIs.
File Formats \& Data Serialization: Understanding of various file formats and ability to convert between them.
Operating Systems \& Scripting: Comfortable with Linux shell and basic shell scripting.
Version Control \& CI/CD: Using Git for source control and setting up CI pipelines for data projects.
Monitoring \& Logging Tools: Utilizing monitoring tools for data workflows.
Data Visualization/Verification: Basics of tools like Excel or Python’s Jupyter notebooks for data sanity checks.
Security \& Networking: Understanding network configurations for data transfer.
Testing Frameworks: Familiarity with PyTest or unittest for writing tests for data transformations.
Collaboration Tools: Experience with tools like JIRA and documentation tools.
AI/ML Familiarity: Bonus if you understand some AI/ML fundamentals
Salary Range: 124300\-168100 a year
\#LI\-MM6
Location
Seattle, WA
Job Function
CONSULTANCY
Role
Engineer
Job Id
415327
Desired Skills
Artificial Intelligence
Salary Range
$124,300\-$168,100 a year
Salary Context
This $124K-$168K range is below the median 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 Tata Consultancy Services (TCS), 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($146K) sits 19% below the category median. Disclosed range: $124K to $168K.
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.
Tata Consultancy Services (TCS) AI Hiring
Tata Consultancy Services (TCS) has 27 open AI roles right now. They're hiring across AI/ML Engineer, AI Consultant, Data Scientist, AI Architect. Positions span Sunrise, FL, US, Atlanta, GA, US, Austin, TX, US. Compensation range: $90K - $210K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% 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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